Search

Background: Aspergillus spp. infection might induce negative effects on peanut seeds, including decreased germination rates and suppressed seedling vigor. Furthermore, A. flavus can secret aflatoxin, regarding food safety and human health. The prolonged use of fungicides for treating mold infections has raised concerns regarding the emergence of fungicide-resistant strains, environmental pollution, and adverse effects on human health. The usage of lactic acid bacteria, including Lactiplantibacillus plantarum and Levilactobacillus brevis for the management of plant diseases, has garnered increasing attention in recent years as a viable alternative to chemical-based therapies. This study aimed to investigate the efficacy of LABs in pre-treating peanut seeds as a biological solution against A. flavus infection before cultivation. Results: Lactiplantibacillus plantarum and Levilactobacillus brevis have demonstrated the ability to suppress A. flavus in vitro. In the in vivo investigation, pre-treatment of peanut seeds with cell-free supernatant derived from L. plantarum (LP-CFS) and L. brevis (LB-CFS) significantly reduced A. flavus infection levels. The conidial count decreased from 8.63 log conidia/g in the untreated group to 5.35 log conidia/g with LP-CFS treatment and 4.59 log conidia/g with LB-CFS treatment. Additionally, A. flavus infection reduced the germination rate of peanut seeds to only 20.4% compared to 63.6% in the control group. In comparison, pre-treatment with LP-CFS and LB-CFS increased the germination rate to 75.6% and 76.8%, respectively, and further improved the vigor index in A. flavus-infected peanut seeds. Conclusion: The present findings indicated that bioactive compounds derived from L. plantarum and L. brevis emerge as promising candidates for treating peanut seeds, effectively protecting them against A. flavus infection. Moreover, these compounds facilitate the growth of seedlings, which could be a potential alternative to chemical fungicides, and contribute to sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Bacterial plant diseases are difficult to control as the durability of deployed control measures is thwarted by continuous and rapid changing of bacterial populations. Although application of copper compounds to plants is the most widespread and inexpensive control measure, it is often partially efficacious for the frequent appearance of copper-resistant bacterial strains and it is raising concerns for the harmful effects of copper on environment and human health. Consequently, European Community included copper compounds in the list of substances candidates for substitution. Nanotechnologies and the application of nanoparticles seem to respond to the need to find new very effective and durable measures. We believe that Argirium-SUNCs®, silver ultra nanoclusters with an average size of 1.79 nm and characterized by rare oxidative states (Ag2+/3+), represent a valid candidate as a nano-bactericide in the control of plant bacterial diseases. Respect to the many silver nanoparticles described in the literature, Argirium-SUNCs have many strengths due to the reproducibility of the synthesis method, the purity and the stability of the preparation, the very strong (less than 1 ppm) antimicrobial, and anti-biofilm activities. In this mini-review, we provide information on this nanomaterial and on the possible application in agriculture. Key points: • Argirium-SUNCs have strong antimicrobial activities against phytopathogenic bacteria. • Argirium-SUNCs are a possible plant protection product. • Argirium-SUNCs protect tomato plants against bacterial speck disease. [ABSTRACT FROM AUTHOR]

Aims: Damping-off disease, caused by Fusarium oxysporum, affects the growth of Pinus massoniana seedlings. Plant growth-promoting fungi and rhizobacteria (PGPF and PGPR) are widely used in agriculture to control plant soil-borne disease, however, the joint mechanism by which they inhibit damping-off disease in forestry requires further exploration. Methods: The current study screened for the ability of antagonistic PGPF and PGPR strains to inhibit the pathogen, and used soil microbiome and plant transcriptome technologies to characterize the biocontrol mechanism. Results: PGPF strain 3Y, identified as Trichoderma longibrachiatum, and PGPR strain K29, identified as Burkholderia stabilis, were screened and found to strongly inhibit the growth of F. oxysporum through direct contact with the hyphae. The combined use of T. longibrachiatum and B. stabilis effectively reduced disease incidence and severity, and promoted the growth of P. massoniana seedlings, and enhanced soluble sugar, proline, SOD and POD activities. Compound strains treatment impacted the structure of rhizosphere bacterial microbial community, causing significant differences in the relative abundances of some key phyla and genera, promoting the enrichment of some beneficial microorganisms. Transcriptome profiles showed that combination treatment with the biocontrol strains induced the expressions of 8541 differentially expressed genes (DEGs). These genes participated in key biological pathways associated with starch and sucrose metabolism, plant hormone signal transduction, photosynthesis, antioxidant enzymes, and proline synthesis. Conclusion: The combined use of PGPF and PGPR strains controlled F. oxysporum infection of P. massoniana seedlings by regulating physiological responses and soil microbial community. [ABSTRACT FROM AUTHOR]

Plant disease diagnosis in smart agriculture is a crucial issue that carries substantial economic significance on a global scale. To address this challenge, intelligent and smart agricultural solutions are currently being developed to assist farmers in implementing preventive measures to increase crop production. As deep learning technology continues to evolve, many convolutional neural network (CNN) models have emerged as highly effective for detecting plant leaf diseases. These CNN-based models require heavy computation and processing cost. So, this paper develops a new lightweight deep convolutional neural network named lightweight DenseNet (LWDN) for detection of plant leaf disease for agricultural applications. Based on the DenseNet121 architecture, the presented model comprises pruned and concatenated architecture of DenseNet121. The presented study involved training and testing a proposed model (LWDN) on the PlantVillage dataset to acquire a knowledge of plant disease features. The model was trained using a combination of partial layer freezing, transfer learning, and feature fusion techniques. Out of several models experimented with, the proposed model has 99.37% classification accuracy, a model size of 13.8 MB, with 1.5 M parameters. The proposed model has 93% fewer parameters than InceptionV3 and Xception and 90% and 50% fewer parameters compared to VGG16 and MobileNetV2, respectively. Furthermore, the proposed method has superior diagnostic capabilities compared to several prior studies and larger state-of-the-art models utilizing plant leaf images. The compact size and competitive accuracy of the LWDN model render it appropriate for real-time plant diagnosis on portable and mobile devices with restricted computational resources. [ABSTRACT FROM AUTHOR]

Disease is a serious problem in tomato plants, causing huge economic losses. Disease detection, as the premise of protection, is important. This paper employed Electronic nose (E-nose) and Gas Chromatography–Mass Spectrometer (GC–MS), as an auxiliary technique, to predict disease type and its severity in the tomato plant. Twenty-five volatile constituents were identified using GC–MS. Their concentrations were calculated and showed the difference in different groups. Furthermore, the results of E-nose and GC–MS were compared and showed a good correlation. Moreover, the possibility of E-nose in classifying tomato plants infected with different types and severities of disease either respectively or together was proved based on either Principal Component Analysis (PCA) or Discriminant Functions Analysis (DFA). Then, Backpropagation neural network (BPNN) was introduced and showed that the correct classification rates were 98.3% for the training set and 97.5% for the testing set for predicting disease type and severity. Moreover, 100% correct classification rate was obtained for the diseased groups, which was very meaningful for the prevention of disease spread and met actual application needs. This study demonstrates the feasibility of E-nose in predicting tomato plants infected with disease in different types and severities. [ABSTRACT FROM AUTHOR]

Learning to recognize novel visual classes from few samples is challenging but promising. Previous studies have shown that few-shot model tends to overfit and lead to poor generalization performance, which is because it finds a biased distribution based on a few samples. In addition, in agriculture-specific domains, there are more serious research challenges such as imbalanced disease distribution, one-shot representation biases, fine-grained recognition, and granularity shift. As far as we know, this study is the first work on the fine-grained "Coarse-to-Fine" few-shot plant disease classification, which classifies "fine-grained novel classes" (specific to disease severity) based on "coarse-grained base classes" (specific to plant species). A complete two-stage in-use calibration strategy is presented in this paper. Firstly, we propose an attention-based inverse Mahalanobis distance weighted prototype calibration module (AIPCM). By transferring statistics from sample-rich coarse-grained base classes to sample-scarce fine-grained novel classes, we achieve prototype calibration for 1-shot sample and obtain an unbiased distribution in the feature space. Secondly, to generate more reasonable decision boundaries, we propose a prior-driven task-adapted decision boundary calibration module (TDBCM) based on class-covariance metric. The original Euclidean/Cosine distance is updated to the Mahalanobis distance by introducing the prior mean and covariance of the high-dimensional features. Experimental results on several datasets demonstrate that our model outperforms the state-of-the-art (SOTA) models. It can be said that our work is a valuable supplement to the domain-specific agricultural applications. [ABSTRACT FROM AUTHOR]

Ralstonia solanacearum, the bacterium that causes bacterial wilt, is a destructive phytopathogen that can infect over 450 different plant species. Several agriculturally significant crop plants, including eggplant, tomato, pepper, potato, and ginger, are highly susceptible to this plant disease, which has a global impact on crop quality and yield. There is currently no known preventive method that works well for bacterial wilt. Bacteria use two-component systems (TCSs) to sense their environment constantly and react appropriately. This is achieved by an extracellular sensor kinase (SK) capable of sensing a suitable signal and a cytoplasmic response regulator (RR) which gives a downstream response. Moreover, our investigation revealed that R. solanacearum GMI1000 possesses a substantial count of TCSs, specifically comprising 36 RRs and 27 SKs. While TCSs are known targets for various human pathogenic bacteria, such as Salmonella, the role of TCSs in R. solanacearum remains largely unexplored in this context. Notably, numerous inhibitors targeting TCSs have been identified, including GHL (Gyrase, Hsp, and MutL) compounds, Walk inhibitors, and anti-TCS medications like Radicicol. Consequently, the investigation into the involvement of TCSs in virulence and pathogenesis has gained traction; however, further research is imperative to ascertain whether TCSs could potentially supplant conventional anti-wilt therapies. This review delves into the prospective utilization of TCSs as an alternative anti-wilt therapy, focusing on the lethal phytopathogen R. solanacearum. [ABSTRACT FROM AUTHOR]

DNA marker technology, being an alternative to classical hybrid selection approach, is a rapid method for identifying genes and loci associated with breeding-important traits at the early stages of ontogenesis, and, consequently, represents an approach for intensifying the breeding process. This review presents an overview of the studies on the identification of the economically important traits in the pear genome, as well as the development and use of marker-assisted selection (MAS) techniques. To date, a number of genetic factors have been identified in the pear genome, including the resistance to diseases and pests, namely, the resistance to European pear scab (V. pirina Aderh) and Asian pear scab (V. nashicola), to black spot (Alternaria alternata (Fr.) Keissler) and brown spot (Stemphylium vesicarium) disease, fire blight (Erwinia amylovora), pear psylla (Cacopsylla pyri), pear slug (Caliroacerasi), pear blister mite (Eriophyes pyri), self-incompatibility locus, and dwarf trait gene. Major genes and quantitative trait loci (QTLs) for the fruit quality characteristics have also been identified, namely those controlling fruit skin color and fruit russeting, fruit size and weight, taste, the level of ethylene production, and harvest time. It should be noted that Russian studies in this field are limited to the validation and use of MAS techniques developed abroad. According to the experience of Japanese researchers, the use of MAS for several key traits had considerably increased the efficiency of breeding process. Despite the currently limited list of MAS techniques for pears, the high pace of genome research guarantees fairly rapid development of new methods in the future. In combination with new breeding technologies (NBT), the use of MAS for pear is a promising trend in breeding. [ABSTRACT FROM AUTHOR]

Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively. [ABSTRACT FROM AUTHOR]

The genus Trichoderma comprised important antagonists of pathogenic fungi and can be used in agriculture to combat various plant diseases. In the course of the present work, two micromycete strains were isolated from wood cuts in Eastern Siberia, which were identified by morphological and molecular genetic characteristics as Trichoderma atroviride and Trichoderma harzianum. These Trichoderma strains efficiently inhibited the development of the pathogenic fungi studied (by up to 80%). The data presented in the paper indicate that Trichoderma atroviride and Trichoderma harzianum may be promising for further study of the means of biocontrol of plant diseases. [ABSTRACT FROM AUTHOR]

Plant pathogenic viruses rely on insect vectors for transmission. Among insects, hemipterans, including aphids, leafhoppers, plant hoppers, whiteflies, mealybugs, true bugs, and certain treehoppers, are predominant as vectors for plant viruses. Plant viruses contribute to more than 47% of worldwide plant diseases. Bemisia tabaci is one of the most threatening pests in agriculture, causing significant losses to many important crops on a global scale. Among the most destructive viruses facilitated by whiteflies are begomoviruses (Geminiviridae), forming an emergent and critical group of plant pathogens solely transmitted by the sweet-potato whitefly Bemisia tabaci. In this comprehensive review, we delve into the wealth of data concerning the diverse transmission capacities of various whitefly species, including cryptic members of the B. tabaci complex, in their role as vectors for begomovirus and other viruses. We presented a meticulous analysis of the factors influencing viral transmission by whiteflies, shedding light on the intricate interplay of viruse and their insect vectors. Moreover, this review serves as a crucial reference, presenting a profound exploration of the intricate relationships between whitefly vectors and begomoviruses, and providing valuable insights for the scientific community working towards the sustainable management of plant diseases in the face of evolving challenges posed by these complex biological systems. [ABSTRACT FROM AUTHOR]

Plant leaf disease identification and classification are the most essential and demanding tasks in the agriculture field. In traditional researches, various automated detection technologies have been developed with the goal of more accurately identifying plant leaf disease. Nevertheless, it faces some problems related to complex mathematical modeling, increased time consumption, processing overhead, and mis-prediction results. Therefore, a novel probabilistic intermittent neural network and artificial jelly fish optimization-based plant leaf disease detection system is proposed in this paper. The proposed work aims to "make a new detection scheme to identify correctly plant leaf disease from the given dataset." Here, the probabilistic intermittent neural network (PINN) classification technique is used to predict label as normal or affected by disease. If it is disease affected, the residual multi-scale Unet segmentation (RMUNet) segmentation technique is applied to segment the disease affected region. Finally, the simulation outcomes confirm the efficiency of the proposed leaf disease identification system under some variables. [ABSTRACT FROM AUTHOR]

Pecan (Carya illinoinensis) is one important exotic forest crop cultivated in South America, specifically in Brazil, Uruguay, and Argentina. However, diseases such as anthracnose, favored by high humidity conditions and high summer temperatures, make its cultivation difficult, causing important loss to pecan farmers. This study used morphological and molecular approaches to identify the Colletotrichum species causing anthracnose in pecan plantations in Southern Brazil. The isolates obtained from pecan fruits with anthracnose symptoms were grouped through quantitative morphological characteristics into three distinct morphotypes. Molecular analysis of nuclear genes allowed the identification of six species of Colletotrichum causing anthracnose in pecan: C. nymphaeae, C. fioriniae, C. gloeosporioides, C. siamense, C. kahawae, and C. karsti. Three of these species are reported for the first time as causal agents of anthracnose in pecan. Therefore, these results provide an important basis for the adoption and/or development of anthracnose management strategies in pecan orchards cultivated in southern Brazil and neighboring countries. [ABSTRACT FROM AUTHOR]

Objectives: Peri-implant diseases, being the most common implant-related complications, significantly impact the normal functioning and longevity of implants. Experimental models play a crucial role in discovering potential therapeutic approaches and elucidating the mechanisms of disease progression in peri-implant diseases. This narrative review comprehensively examines animal models and common modeling methods employed in peri-implant disease research and innovatively summarizes the in vitro models of peri-implant diseases. Materials and methods: Articles published between 2015 and 2023 were retrieved from PubMed/Medline, Web of Science, and Embase. All studies focusing on experimental models of peri-implant diseases were included and carefully evaluated. Results: Various experimental models of peri-implantitis have different applications and advantages. The dog model is currently the most widely utilized animal model in peri-implant disease research, while rodent models have unique advantages in gene knockout and systemic disease induction. In vitro models of peri-implant diseases are also continuously evolving to meet different experimental purposes. Conclusions: The utilization of experimental models helps simplify experiments, save time and resources, and promote advances in peri-implant disease research. Animal models have been proven valuable in the early stages of drug development, while technological advancements have brought about more predictive and relevant in vitro models. Clinical relevance: This review provides clear and comprehensive model selection strategies for researchers in the field of peri-implant diseases, thereby enhancing understanding of disease pathogenesis and providing possibilities for developing new treatment strategies. [ABSTRACT FROM AUTHOR]

Cucumber is one of the most popular vegetable varieties, but leaf disease of cucumber is the key factor restricting the increase of yield. Common cucumber diseases include downy mildew, powdery mildew and gray mold. The plant electronic medical records (PEMRs) formed by "plant clinic" are the diagnosis record of the real disease occurrence issued by the plant doctor, which provides a new idea for the cucumber disease diagnosis. The efficient mining of prescription big data to facilitate precise diagnoses of crop pests and diseases represents an emerging challenge. The data mining technology represented by machine learning has attracted wide attention. Therefore, 15 diagnosis models are proposed to deal with this problem. Since SVM has many advantages including implementing the structural risk minimization principle and can effectively deal with the small sample data, five algorithms based on SVM have achieved better diagnosis performance in comparison with others. Moreover, the highest prediction accuracy is beyond 80%. In addition, the prediction performance has been improved after employing the undersampling technology for the imbalanced data. This means they are suitable for this cucumber diseases diagnosis. [ABSTRACT FROM AUTHOR]

The vector or pest control is essential to reduce the risk of vector-borne diseases or crop losses. Among the available biological control tools, the sterile insect technique (SIT) is one of the most promising. However, SIT-control campaigns must be carefully planned in advance in order to render desirable outcomes. In this paper, we propose SIT-control intervention programs that can avoid the real-time monitoring of the wild population and require to mass-rear a minimal overall number of sterile insects, in order to induce a local elimination of the wild population in the shortest time. Continuous-time release programs are obtained by applying an optimal control approach, and then laying the groundwork of more practical SIT-control programs consisting of periodic impulsive releases. [ABSTRACT FROM AUTHOR]

Crop diseases are a huge threat to food security, yet timely detection is a difficult task due to the absence of infrastructure in various regions of the world. In agriculture, the detection of disease in plants is complex because farmers must often evaluate whether the crop that are harvesting appears good enough. It is crucial to treat this seriously because it may result to major effects on plants, affecting product characteristics, quantity, or overall productivity. Plant illnesses produce outbreaks of disease on a systematic interval, resulting in large-scale fatalities and a substantial economic impact. Early and precise tools for diagnosing plant diseases are essential for robust plant production and for reducing both qualitative and quantitative losses in crop yield. Cutting-edge and creative data analysis technologies significantly aid in the accurate and precise identification of diseases. Among all the crops, tomato plants are widely grown and required in all parts of the world. Given all the above challenges, this study seeks to recognize tomato plant diseases in an accurate and timely manner. In this paper, a multi-objective hybrid fruit fly optimization algorithm that relies on simulated annealing optimized SVM is proposed to identify tomato plant diseases at an earlier stage in an accurate manner avoiding global optimization problems. The hybridization of simulated annealing with FOA helps in reducing the hyperparameter problems. The proposed methodology was tested and experimented extensively and the results enlightened that the proposed methodology achieved 91.1% accuracy and reliability and the experimental observations also indicated that the suggested method overcomes the drawbacks of the current algorithms. In addition, the operational efficiency of the proposed system was measured on statistical parameters like accuracy (91.1%), sensitivity (96.7%), precision (91.8%), specificity (91.2%), and F1-score (94.5%). Also, a comparison analysis with existing algorithms like DT, RF, KNN, and K-means with SVM was also performed, and overall, it was concluded that proposed methodology is having high methodological approach for diagnosing crop diseases. [ABSTRACT FROM AUTHOR]

Carbendazim (CBD) is widely used as a fungicide that acts as a pesticide in farming to prevent crop diseases. However, CBD can remain on crops for a long time. When consumed by humans and animals, it produces a range of toxic symptoms and poses a serious threat to their health. Therefore, the detection of CBD is necessary. Traditional assay strategies for CBD detection, although sensitive and practical, can hardly achieve fast, robust monitoring during food processing and daily life. Here, we designed a novel electrochemical sensor for CBD detection. In this method, iron oxyhydroxide nanomaterial (β-FeOOH) was first prepared by hydrothermal method. Then, a molecularly imprinted polymer (MIP) layer was electropolymerized on the surface using CBD as the template and resorcinol (RC) as the functional monomer. The synergistic interaction between β-FeOOH and MIP endows the MIP/β-FeOOH/CC-based electrochemical sensor with high specificity and sensitivity. Under optimal conditions, the MIP/β-FeOOH/CC-based sensor showed a wide linear range of 39 pM–80 nM for CBD and a detection limit as low as 25 pM. Therefore, the as-prepared sensor can be a practical and effective tool for pesticide residue detection. [ABSTRACT FROM AUTHOR]

Coffee production is significantly impacted by various diseases, predominantly those caused by fungi. One such notable disease in coffee crops is caused by the fungus Phoma spp. This pathogen leads to several symptoms detrimental to coffee plants, such as leaf lesions, drying of branches, and rotting of flowers and fruits. These symptoms often result in the dropping of the affected parts, subsequently leading to a decrease in the overall yield of the coffee crop. In response to this challenge, our objective was to develop a forecasting model for the incidence of Phoma leaf spot in Brazilian coffee crops, utilizing advanced machine learning algorithms. This approach is intended to predict disease outbreaks, thereby enabling timely and effective management strategies to mitigate the impact on coffee yield. The study was conducted in two stages: (1) calibration of machine learning models for locations (Boa Esperança, Carmo de Minas, Muzambinho, Varginha, Araxá, Araguari, and Patrocínio) with field data between 2010 and 2022; (2) Phoma leaf spot incidence forecast in municipalities of coffee-producing states in Brazil [Paraná (PR), São Paulo (SP), Rio de Janeiro (RJ), Espírito Santo (ES), Minas Gerais (MG), Goiás (GO), and Bahia (BA)]. Thirty-year climate data were retrieved from the NASA/POWER platform. Reference evapotranspiration was estimated by the Penman–Monteith method, generating water balance according to Thornthwaite and Mather (1955). To understand the effect of climate variables on the disease incidence, Pearson's univariate correlation was performed for each location. We used six algorithms to forecast the disease incidence, considering a 7-day latency period to define input variables. It is noteworthy that the evaluated locations present similar climatic conditions. Summer was the hottest and rainiest period, while winter was the coldest and driest. Annual averages of air temperature, cumulative rainfall, potential evapotranspiration, soil water storage, and incident radiation were 21.1 °C, 1208.9 mm, 1283.2 mm, 58.0 mm, 435.7 mm, and 18.1 MJ m2 day−1, respectively. The XGBoost model demonstrated superior performance for both high- and low-yield coffee trees, achieving an impressive precision (R2fit) of 0.46 and 0.51, respectively. Additionally, it exhibited high accuracy, with Root Mean Square Error (RMSE) values of 3.45% for high-yielding and 3.16% for low-yielding trees. In contrast, the multilayer perceptron (MLP) model displayed suboptimal results under both yield conditions. Given these findings, the XGBoost model proves effective in predicting the incidence of the disease at least 7 days ahead, based on the parameters applied in this study. [ABSTRACT FROM AUTHOR]

This study introduces YOLOv8n-vegetable, a model designed to address challenges related to imprecise detection of vegetable diseases in greenhouse plant environment using existing network models. The model incorporates several improvements and optimizations to enhance its effectiveness. Firstly, a novel C2fGhost module replaces partial C2f. with GhostConv based on Ghost lightweight convolution, reducing the model's parameters and improving detection performance. Second, the Occlusion Perception Attention Module (OAM) is integrated into the Neck section to better preserve feature information after fusion, enhancing vegetable disease detection in greenhouse settings. To address challenges associated with detecting small-sized objects and the depletion of semantic knowledge due to varying scales, an additional layer for detecting small-sized objects is included. This layer improves the amalgamation of extensive and basic semantic knowledge, thereby enhancing overall detection accuracy. Finally, the HIoU boundary loss function is introduced, leading to improved convergence speed and regression accuracy. These improvement strategies were validated through experiments using a self-built vegetable disease detection dataset in a greenhouse environment. Multiple experimental comparisons have demonstrated the model's effectiveness, achieving the objectives of improving detection speed while maintaining accuracy and real-time detection capability. According to experimental findings, the enhanced model exhibited a 6.46% rise in mean average precision (mAP) over the original model on the self-built vegetable disease detection dataset under greenhouse conditions. Additionally, the parameter quantity and model size decreased by 0.16G and 0.21 MB, respectively. The proposed model demonstrates significant advancements over the original algorithm and exhibits strong competitiveness when compared with other advanced object detection models. The lightweight and fast detection of vegetable diseases offered by the proposed model presents promising applications in vegetable disease detection tasks. [ABSTRACT FROM AUTHOR]

Plant leaf diseases are a major cause of plant mortality, especially in crops. Timely and accurately identifying disease types and implementing proper treatment measures in the early stages of leaf diseases are crucial for healthy plant growth. Traditional plant disease identification methods rely heavily on visual inspection by experts in plant pathology, which is time-consuming and requires a high level of expertise. So, this approach fails to gain widespread adoption. To overcome these challenges, we propose a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network (ERCP-Net). It consists of channel extension residual block (CER-Block), adaptive channel attention block (ACA-Block), and bidirectional information fusion block (BIF-Block). Meanwhile, an application for the real-time detection of plant leaf diseases is being created to assist precision agriculture in practical situations. Finally, experiments were conducted to compare our model with other state-of-the-art deep learning methods on the PlantVillage and AI Challenger 2018 datasets. Experimental results show that our model achieved an accuracy of 99.82% and 86.21%, respectively. Also, it demonstrates excellent robustness and scalability, highlighting its potential for practical implementation. [ABSTRACT FROM AUTHOR]

Bean is a widely cultivated crop worldwide; however, it is susceptible to various diseases that can adversely affect the quality of beans, including rust and angular leaf spot diseases. These diseases can cause significant damage by wiping out hectares of crops, emphasizing the need for early detection. Deep learning algorithms have shown remarkable performance in image detection tasks, achieving high accuracy on many datasets. This study used a deep learning technique, specifically the MobileNet architecture, to detect bean leaf disease. We evaluated the effectiveness of the approach by testing the model on three different bean leaf image datasets with varying difficulty. MobileNet was chosen due to its ability to achieve high performance with a reduced number of parameters and faster execution time. Additionally, we examined the impact of the datasets on the model's performance and presented a comparative analysis of the three datasets, and then we applied the GradCAM technique to the model's predictions. Experimental results showed that the proposed approach achieved remarkable accuracy, with over 92% accuracy on all three datasets. The study provides a valuable contribution to the field of plant disease detection and highlights the potential of deep learning techniques in detecting bean leaf disease. [ABSTRACT FROM AUTHOR]

Aim: Entomopathogenic fungi infect and kill a wide range of insects and other arthropod pests. They exhibit versatility as biological control agents, capable of targeting a wide range of hosts. Additionally, they have been used to develop various formulations of agricultural biopesticides. Although this sole role of pest management that has traditionally been ascribed to entomopathogenic fungi is well documented, recent findings provided evidence of other possible functions that can be harnessed to enhance soil–plant systems' productivity. Methods: This study examines that aspect of entomopathogenic fungi through a comprehensive review of the literature and metadata and discusses the potential and prospects for further increased acceptance in the field. The first segment of this study primarily focuses on analysing the existing metadata, whereas the subsequent discussion section provides in-depth review of the topic. Results: It was revealed that entomopathogenic fungi positively influence soil aggregation through various mechanisms including the production of glomalin-like substances and other glycoprotein polysaccharides. Additionally, these fungi contribute to organic matter and nutrient recycling, promote plant growth, and facilitate overall soil health. Furthermore, they were found to initiate induced resistance in crop plants, thereby aiding in the prevention of herbivorous feeding, alleviation of stress, and reduction of diseases in the host plant. Conclusion: Although some studies have reported limitations associated with these fungi, such as lengthy killing time, environmental specificity, toxicosis production, and challenges regarding isolation and characterization, we have reached the conclusion that these ubiquitous fungi may be important in nutrient recycling, soil health improvement, and as bodyguards for plant growth and productivity. More studies are still needed to understand the underlying mechanisms, and it should be added that different experimental designs can lead to contradictory results. [ABSTRACT FROM AUTHOR]

The Bacillus genus has emerged as an important player in modern agriculture, revolutionizing plant growth promotion through recent advances. This review provides a comprehensive overview of the critical role Bacillus species play in boosting plant growth and agricultural sustainability. Bacillus genus bacteria benefit plants in a variety of ways, according to new research. Nitrogen fixation, phosphate solubilization, siderophore production, and the production of growth hormones are examples of these. Bacillus species are also well-known for their ability to act as biocontrol agents, reducing phytopathogens and protecting plants from disease. Molecular biology advances have increased our understanding of the complex interplay between Bacillus species and plants, shedding light on the genetic and metabolic underpinnings of these interactions. Furthermore, novel biotechnology techniques have enabled the development of Bacillus-based biofertilizers and biopesticides, providing sustainable alternatives to conventional chemical inputs. Apart from this, the combination of biochar and Bacillus species in current biotechnology is critical for improving soil fertility and encouraging sustainable agriculture through enhanced nutrient retention and plant growth. This review also emphasizes the Bacillus genus bacteria's ability to alleviate environmental abiotic stresses such as drought and salinity, hence contributing to climate-resilient agriculture. Moreover, the authors discuss the challenges and prospects associated with the practical application of Bacillus-based solutions in the field. Finally, recent advances in Bacillus-mediated plant growth promotion highlight their critical significance in sustainable agriculture. Understanding these improvements is critical for realizing the full potential of Bacillus genus microorganisms to address current global food production concerns. [ABSTRACT FROM AUTHOR]

Plant disease diagnosis is one of the latest critical research areas of sustainable agriculture. The evolution of computer vision-based systems in order to identify, classify and localize diseases has automated the process of plant disease identification. CNNs are the pre-eminent deep learning-based algorithms used to automate plant disease recognition that has proven decisive on various benchmarks. However, a substantial part of the research lacks adequate attention to specific issues like the unavailability of datasets, high annotation costs and non-conformity of the models. Therefore, there is a pressing need to exploit the latest trends and technologies in this area to solve the above-mentioned problems. As a step ahead in this direction, a new framework has been proposed using semi-supervised & ensemble learning. The proposed framework is validated through a series of experiments on benchmark datasets. The results reported a significant performance improvement in classifying plant diseases, outperforming existing works with an improvement of 18.03% and 15% regarding the accuracy and F1 score, respectively. The mean average precision for detection is improved by 13.35%. Findings from this research will be beneficial for farmers, plant pathologists and researchers, which in turn will strengthen the sustainable facet of agriculture. [ABSTRACT FROM AUTHOR]

Pathogenic bacteria are responsible for causing numerous diseases in humans and plants. Unfortunately, the effectiveness of systematic bactericidal agents is gradually diminishing due to the rise of bacterial resistance. However, a potential solution lies in chitosan, a natural polymer renowned for its remarkable bactericidal properties. In this study, chitosan (CHT)-metal complexes were synthesized using monovalent (Ag+), bivalent (Ni2+, Cu2+, Mn2+), and trivalent (Fe3+) metal ions, and their bactericidal activity was investigated against gram-positive and gram-negative bacteria strains. The association of the metal centers in the chitosan backbone increases its bactericidal effect for all tested cases. Notably, the Ag/Chitosan complex displayed the highest bactericidal effect, as expected due to the intrinsic activity of Ag+ ions. Intriguingly, even metal ions lacking with intrinsic bactericidal effect, demonstrated enhancement when complexed with chitosan. These findings strongly suggest that the inhibitory properties of the complexes are linked to the nature of the coordinated bond established between the metal center and the chitosan ligand. [ABSTRACT FROM AUTHOR]

Transcription factor Cmr1 (Colletotrichum melanin regulation 1) and its homologs in several plant fungal pathogens are the regulators of the 1,8-dihydroxynaphthalene (DHN)-melanin biosynthesis pathway and have evolved functional diversification in morphology and pathogenicity. The fungal genus Alternaria comprises the group of "black fungi" that are rich in DHN-melanin in the primary cell wall and septa of the conidia. Some Alternaria species cause many economically important plant diseases worldwide. However, the evolution and function of Cmr1 homologs in Alternaria remain poorly understood. Here, we identified a total of forty-two Cmr1 homologs from forty-two Alternaria spp. and all contained one additional diverse fungal specific transcription factor motif. Phylogenetic analysis indicated the division of these homologs into five major clades and three branches. Dated phylogeny showed the A and D clades diverged latest and earliest, respectively. Molecular evolutionary analyses revealed that three amino acid sites of Cmr1 homologs in Alternaria were the targets of positive selection. Asmr1, the homolog of Cmr1 in the potato early blight pathogen, Alternaria solani was amplified and displayed the sequence conservation at the amino acid level in different A. solani isolates. Asmr1 was further confirmed to have the transcriptional activation activity and was upregulated during the early stage of potato infection. Deletion of asmr1 led to the decreased melanin content and pathogenicity, deformed conidial morphology, and responses to cell wall and fungicide stresses in A. solani. These results suggest positive selection and functional divergence have played a role in the evolution of Cmr1 homologs in Alternaria. Key points: • Cmr1 homologs were under positive selection in Alternaria species • Asmr1 is a functional transcription factor, involved in spore development, melanin biosynthesis, pathogenicity, and responses to cell wall and fungicide stresses in A. solani • Cmr1 might be used as a potential taxonomic marker of the genus Alternaria [ABSTRACT FROM AUTHOR]

Habanero plant diseases can significantly reduce crop yield and quality, making early detection and treatment crucial for farmers. In this study, we discuss the creation of a modified VGG16 (MVGG16) Deep Transfer Learning (DTL) model-based smartphone app for identifying habanero plant diseases. With the help of the smartphone application, growers can quickly diagnose the health of a habanero plant by taking a photo of one of its leaves. We trained the DTL model on a dataset of labelled images of healthy and infected habanero plants and evaluated its performance on a separate test dataset. The MVGG16 DTL algorithm had an accuracy, precision, f1-score, recall and AUC of 98.79%, 97.93%, 98.44%, 98.95 and 98.63%, respectively, on the testing dataset. The MVGG16 DTL model was then integrated into a smartphone app that enables users to upload photographs, get diagnosed, and explore a history of earlier diagnoses. We tested the software on a collection of photos of habanero plant leaves and discovered that it was highly accurate at spotting infected plants. The smartphone software can boost early identification and treatment of habanero plant diseases, resulting in higher crop output and higher-quality harvests. [ABSTRACT FROM AUTHOR]

The primary source of food is extracted from the plant. Take care of and maintain the plants in real-time to enhance human survival. Diseases in plants can directly lead to a reduction in the industrial economy. Automatic detection of plant diseases is crucial for effective disease control. Vitis vinifera (Grapes) is an essential crop with rich vitamin C nutrients. This paper targets to classify the major diseases in Vitis vinifera leaves using Capsule Network (CapsNet), that prevails over the significant CNN limitations by discarding the pooling layers and adding capsule layers. Dynamic routing techniques of CapsNet make are more robust for the affine transformation of the leaves dataset. It is capable of learning large datasets effectively with vital image transformations such as rotations and transitions. Implementing CapsNet for Vitis vinifera leaf disease classification, which utilizes dynamic routing between capsules, is a novel method. The proposed CapsNet for disease classification is trained with augmented and non-augmented datasets. The performance metrics highlight that the proposed method can effectively classify the Vitis vinifera plant leaves with 98.7% of validation accuracy. The results highlight that the proposed model performs well in detecting and classifying the diseases of Vitis vinifera plant leaves on two benchmark datasets. [ABSTRACT FROM AUTHOR]

The rhizosphere microbial community is closely associated with plant disease by regulating plant growth, agricultural production, nutrient availability, plant hormone and adaptation to environmental changes. Therefore, it is very important to identify the rhizosphere microbes around plant roots and understand their functions. While studying the differences between the rhizosphere microbiota of healthy and diseased apple trees to find the cause of apple tree disease, we isolated a novel strain, designated as B3-10T, from the rhizosphere soil of a healthy apple tree. The genome relatedness indices between strain B3-10T and other type species of family Chitinophagaceae were in the ranges of 62.4–67.0% for ANI, 18.6–32.1% for dDDH, and 39.0–56.6% for AAI, which were significantly below the cut‑off values for the species delineation, indicating that strain B3-10T could be considered to represent a novel genus in family Chitinophagaceae. Interestingly, the complete genome of strain B3-10T contained a number of genes encoding ACC-deaminase, siderophore production, and acetoin production contributing to plant-beneficial functions. Furthermore, strain B3-10T was found to significantly promote the growth of shoots and roots of the Nicotiana benthamiana, which is widely used as a good model for plant biology, demonstrating that strain B3-10T, a rhizosphere microbe of healthy apple trees, has the potential to promote growth and reduce disease. The phenotypic, chemotaxonomic, phylogenetic, genomic, and physiological properties of this plant growth-promoting (rhizo)bacterium, strain B3-10T supported the proposal of a novel genus in the family Chitinophagaceae, for which the name Rhizosphaericola mali gen. nov., sp. nov. (= KCTC 72123T = NBRC 114178T). [ABSTRACT FROM AUTHOR]

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional Neural Networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an accuracy of 96.39% with minimal loss, relying on a 0.00001 Adam optimizer. By comparing the results with existing state-of-the-art approaches, the study demonstrates the effectiveness of CapsNet in accurately identifying and classifying tomato leaf diseases based on spot shape, color, and location. The findings highlight the potential of CapsNet as an alternative to CNNs for improving disease detection and classification in plant pathology research. Highlights: This paper is interested in tracking plant diseases such as leaf disease in Tomatoes, based on optimized Deep Learning Capsule Net. Early detection of the most common diseases based on leaf images can help us track and enhance food quality. Capsule Networks is an optimized alternative method more efficient than traditional CNN for detecting all leaf images and determining all locations. However, it tackles the shortage of conventional CNN. [ABSTRACT FROM AUTHOR]

The stem rot caused by Sclerotium hydrophilum and false smut caused by Ustilaginoidea virens are two of the major production constraints in rice cultivation in India and other countries. Stem rot and false smut can be effectively controlled with synthetic fungicides. However, the indiscriminate use of chemical fungicides may cause development of resistance among the pathogens. In addition to this, synthetic fungicides also exhibit harmful impacts on the environment. Exploiting microbe-based alternatives for managing plant diseases diminishes public concerns about the ill effects of pesticide usage in crops. In this regard, the present study was designed to investigate the potential of native microbial biocontrol agents (BCAs) from rice rhizosphere for the sustainable management of stem rot and false smut diseases in rice. Potential BCAs and pathogens were identified and characterized through morphological, biochemical, and sanger sequencing techniques. Bio-efficacy tests of potential BCAs against stem rot and false smut diseases on rice under glasshouse conditions indicated higher seed vigour index of the treated seeds, significant improvement in the growth of the seedling, increased dry weight, reduction in percentage disease index viz., 70.03% (stem rot) and 69.24% (false smut) over the control plants. Phytohormones indole acetic acid (IAA), abscisic acid (ABA), gibberellic acid (GA), salicylic acid (SA), and zeatin (tZ) were detected and quantified in the four potential BCAs using liquid chromatography- tandem mass spectrometry (LC–MS/MS). Scanning electron microscopy (SEM) studies revealed the endophytic nature of the strains in rice. The study indicated a positive correlation between the diversity and concentration of phytohormones released by the bioagents and enhanced plant growth promotion and disease suppression in rice. [ABSTRACT FROM AUTHOR]

Early disease identification in crops is critical for food security, especially in Sub-Saharan Africa. To identify cassava diseases, professionals visually score the plants by looking for disease indicators on the leaves which is notoriously subjective. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. However, traditional machine learning algorithms are not calibrated even though they have high accuracy. The ability to provide well-calibrated posterior distributions is one of the most appealing properties of Gaussian processes. Motivated by the current developments in the field of Gaussian Processes, this study proposed a deep Gaussian convolutional neural network model for the detection and classification of cassava diseases using spectral data. The proposed model uses a hybrid kernel function that is the product of a rational quadratic kernel and a squared exponential kernel. Experimental results revealed that our proposed hybrid kernel function performed better in terms of accuracy of 90.1% when compared to both the squared exponential kernel with an accuracy of 88.0% and the rational quadratic kernel with an accuracy of 88.5%. In our future work, we propose to integrate the proposed model with an ensemble of pretrained models, a move that may help to improve the model's performance. [ABSTRACT FROM AUTHOR]

As the lack of plants can affect the energy operation of the entire ecosystem, monitoring and improving the health status of plants is crucial. However, ordinary biosensing platforms lack accuracy and timeliness in monitoring plant growth status. In addition, the prevention and control of plant diseases often involve spraying and administering drugs, which is inefficient and prone to pollution. Microneedles have unique dimensions and shapes, and they have significant advantages as biosensors in the fields of sensing, detection, and drug delivery. Recent evidence suggests that microneedle biosensors can become effective tools for plant diagnosis and treatment. In this review, the comprehensive development of the application of microneedle biosensors in the field of plants is introduced, as well as their manufacturing processes and sensing and detection functions. Furthermore, the application of microneedle biosensors in this field is discussed, and future development directions are proposed. [ABSTRACT FROM AUTHOR]

The aim of the work is the systematic analysis and generalization of the conventional index paradigm of using Earth remote sensing data to assess the state of the soil and vegetation cover. It has been established that the scalar form and the lack of a mathematical basis do not allow the use of conventional vegetation and the similar indices for evaluating the vectors of quantitative indicators of the soil and vegetation cover. At the same time, for making many types of management decisions in agriculture, it is important to construct index images that reflect such qualitative indicators as types of cultivated and weed plants, the presence of plant diseases, damage of crops and soils, and physical and chemical stresses. In terms of informational content, the evaluation of such qualitative states is a procedure for recognizing patterns or classes of soil-and-vegetation complex objects. The subjective empirical approach in choosing the spectral composition of the indices of their combinations, which is currently used, does not currently allow sufficient reliability of such procedures. Therefore, the purpose of the study present is to formalize the process, which enables the empirical approach of constructing indices to be excluded and the entire procedure for their formation for any number and types of recognizable objects to be automated. The basis of formalization is the algorithms for evaluating and selecting the information content of features, followed by the construction of index models, which are linear decision rules for class recognition. The attributes of the classes are the spectral subranges into which the entire spectrum of remote sensing data is divided. The number of informative features is selected from the condition for ensuring the required reliability of recognition of all observed objects (classes). [ABSTRACT FROM AUTHOR]

Tomato is one of the most extensively grown vegetables in any country, and their diseases can significantly affect yield and quality. Accurate and early detection of tomato diseases is crucial for reducing losses and improving crop management. Current deep learning and CNN research have resulted in the availability of multiple CNN designs, making automated plant disease identification viable rather than traditional visual inspection-based disease detection. When using deep learning Methods, the dataset serves one of the most crucial roles in disease prediction. PlantVillage is the most widely used publicly available dataset for tomato disease detection, but it was created in a laboratory/controlled environment, and models trained on it do not perform well on real-world images. Some natural or real-world datasets are available, but they are private and not publicly available. Also, when attempting to predict tomato diseases on the field in the Jodhpur and Jaipur districts of Rajasthan, India, we found that the majority of diseases are leaf miner, spotted wilt virus, and Nnutrition deficiency diseases, but there are no public datasets containing such categories. To overcome these challenges, we propose the creation of a new dataset called "Tomato-Village" with three variants: (a) multiclass tomato disease classification, (b) multilabel tomato disease classification, and (c) object detection-based tomato disease detection. To our best knowledge, "Tomato-Village" will be the first such dataset to be available publicly. Further, we have applied the various CNN architectures/models on this dataset, and baseline results were drawn. The "Tomato-Village" dataset, their baseline results, and related source code will be available at https://github.com/mamta-joshi-gehlot/Tomato-Village. [ABSTRACT FROM AUTHOR]

Background and aims: According to the nitrogen-disease hypothesis, plant diseases will become more serious with the aggravation of global nitrogen (N) deposition. Numerous studies have indicated that Epichloë endophytes can enhance host plant resistance to pathogens. It is unclear how the competitive ability of endophyte-infected (EI) over endophyte-free (EF) plants changes under the interference of N deposition and plant disease. Methods: In this study, Achnatherum sibiricum, native to the Inner Mongolia steppe of China, was used as experimental material. We experimentally manipulated N addition and pathogen inoculation and examined the growth and competition between EI and EF A. sibiricum. Results: The results showed that EI plants had a greater competitive advantage than EF plants under low N conditions, and this advantage decreased with the N supply. When high N and pathogens were both present, pathogen inoculation reversed the adverse effects of high N supply on the competitive advantage of EI A. sibiricum. Epichloë endophytes not only reduced the disease of EI plants but also reduced the disease of neighboring EF plants. Meanwhile, Epichloë endophytes changed the response of the host disease to N. The disease index of EF plants increased with increasing leaf N content, while that of EI plants did not change. Conclusion: This study highlights that under the dual factors of N deposition and pathogen infection, endophytic fungi improve the competitive ability of host plants. Our results show that considering both biotic and abiotic factors is crucial for predicting the dominance of plant-fungal symbionts in the community. [ABSTRACT FROM AUTHOR]

Agriculture is the backbone of the country’s economy. Sugarcane is a globally important crop and a major source of sugar, ethanol and jaggery. One of the problems faced by the sugar cane industry is the diseases that attack the crops. If these diseases are not identified early, they may result in exterminating the whole crops surrounding them. Manually checking each and every corner of a large farm is physically impossible. Machine learning is the contemporary solution to the problem, which can be resolved using the Convolution Neural Network (CNN) techniques. The drone images of all corners of the farm can be fed into the trained model for distinguishing the health status. For training the model the secondary data is taken from Kaggle, which includes both healthy and unhealthy sugarcane plant images, with various diseases in the unhealthy class. Machine learning models effectively identify early-stage crop diseases. This helps the farmer to treat that part of the affected farm quickly in order to avoid the spread of the disease to the remaining parts of the farm. This study deals with sugarcane disease prediction using the CNN model. Two different layered CNN models (VGG-16 and VGG-19) that were tested. The models were trained based on the images of 2165 containing both healthy and unhealthy leaves. The whole dataset is divided into three parts, validating data, testing data, and training data. The selected model – VGG-19 performed better and was found to be analysing the image up to an accuracy of 92% with 90% precision. [ABSTRACT FROM AUTHOR]

Plants regulate root exudates to form the composition of rhizosphere microbial community and resist disease stress. Many studies advocate intervention with biochar (BC) and exogenous microbe to enhance this process and improve plant defenses. However, the mechanism by which BC mediates exogenous microorganisms to enhance root exudate-soil microbial defensive feedback remains unclear. Here, a BC-based Bacillus subtilis SL-44 inoculant (BC@SL) was prepared to investigate the defensive feedback mechanism for plants, which enhanced plant growth and defense more than BC or SL-44 alone. BC@SL not only strengthened the direct inhibition of Rhizoctonia solani Rs by solving the problem of reduced viability of a single SL-44 inoculant but also indirectly alleviated the Rs stress by strengthening plant defensive feedback, which was specifically manifested by the following: (1) increasing the root resistance enzyme activities (superoxide dismutase up to 3.5 FC); (2) increasing the abundance of beneficial microbe in soil (0.38–16.31% Bacillus); and (3) remodeling the composition of root exudates (palmitic acid 3.95–6.96%, stearic acid 3.56–5.93%, 2,4 tert-butylphenol 1.23–2.62%, increasing citric acid 0.94–1.81%, and benzoic acid 0.97–2.13%). The mechanism reveals that BC@SL can enhance the positive regulatory effect between root exudates and microorganisms by optimizing their composition. Overall, BC@SL is a stable and efficient new solid exogenous soil auxiliary, and this study lays the foundation for the generalization and application of green pesticides. Highlights: The problems of effective viable bacteria easy loss were solved by BC@SL. Rs stress was relieved by BC@SL through direct inhibition and defensive feedback. BC@SL optimized defensive feedback by reshaping root exudates and microbiota. [ABSTRACT FROM AUTHOR]

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages. [ABSTRACT FROM AUTHOR]

The expansion of bean genome technologies has prompted new perspectives on generating resources and knowledge essential to research and implementing biotechnological tools for the practical operations of plant breeding programs. This study aimed to resequence the entire genome (whole genome sequencing—WGS) of 40 bean genotypes selected based on their significance in breeding programs worldwide, with the objective of generating an extensive database for the identification of single nucleotide polymorphisms (SNPs). Over 6 million SNPs were identified, distributed across the 11 bean chromosomes. After quality variant filtering, 420,509 high-quality SNPs were established, with an average of 38,228 SNPs per chromosome. These variants were categorized based on their predicted effects, revealing that the majority exerted a modifier impact on non-coding genome regions (94.68%). Notably, a significant proportion of SNPs occurred in intergenic regions (62.89%) and at least one SNP was identified in 58.63% of the genes annotated in the bean genome. Of particular interest, 7841 SNPs were identified in 85% of the putative plant disease defense-related genes, presenting a valuable resource for crop breeding efforts. These findings provide a foundation for the development of innovative and broadly applicable technologies for the routine selection of superior genotypes in global bean improvement and germplasm characterization programs. [ABSTRACT FROM AUTHOR]

The world's population is expected to exceed 9 billion people by 2050, necessitating a 70% increase in agricultural output and food production to meet the demand. Due to resource shortages, climate change, the COVID-19 pandemic, and highly harsh socioeconomic predictions, such a demand is challenging to complete without using computation and forecasting methods. Machine learning has grown with big data and high-performance computers technologies to open up new data-intensive scientific opportunities in the multidisciplinary agri-technology area. Throughout the plant's developmental period, diseases and pests are natural disasters, from seed production to seedling growth. This paper introduces an early diagnosis framework for plant diseases based on fog computing and edge environment by IoT sensors measurements and communication technologies. The effectiveness of employing pre-trained CNN architectures as feature extractors in identifying plant illnesses has been studied. As feature extractors, standard pre-trained CNN models, AlexNet are employed. The obtained in-depth features are eliminated by proposing a revised version of the grey wolf optimization (GWO) algorithm that approved its efficiency through experiments. The features subset selected were used to train the SVM classifier. Ten datasets for different plants are utilized to assess the proposed model. According to the findings, the proposed model achieved better outcomes for all used datasets. As an average for all datasets, the accuracy of the proposed model is 93.84 compared to 85.49, 87.89, 87.04 for AlexNet, GoogleNet, and the SVM, respectively. [ABSTRACT FROM AUTHOR]

Food demand is directly associated with the human population. Due to various plant diseases, there has been a reduction in crop yield. There is an extreme necessity to low such losses in crop yield to meet the rising demand for food. Novel and eco-friendly control approaches should be developed for combating bacterial diseases of crops. Recent control strategies that involve the usage of antibiotics or chemicals are no more effective because of resistance developed by bacterial species. Furthermore, the usage of such agents has proven to be not environmentally friendly. To overcome these issues, bacteriophages are used as an alternative solution. Phages are viruses that attack specific bacterial species, and within current years much consideration is received by them in controlling different diseases caused by bacteria. Phages can be used for controlling different crop-related diseases. Several phage-based products are accessible in the market. Compared to chemical control methods, phage biocontrol offers several advantages. Mixtures of phages can be employed to target pathogenic bacteria. Unlike chemical control strategies, phage mixtures can be readily adjusted to counter any potential resistance. This review summarizes the use of phages as a biocontrol agent against phytopathogens. [ABSTRACT FROM AUTHOR]

Biocontrol agents are safe and effective methods for controlling plant disease pathogens, such as Fusarium solani, which causes dry wilt, and Pectobacterium spp., responsible for potato soft rot disease. Discovering agents that can effectively control both fungal and bacterial pathogens in potatoes has always presented a challenge. Biological controls were investigated using 500 bacterial strains isolated from rhizospheric microbial communities, along with two promising biocontrol strains: Pseudomonas (T17-4 and VUPf5). Bacillus velezensis (Q12 and US1) and Pseudomonas chlororaphis VUPf5 exhibited the highest inhibition of fungal growth and pathogenicity in both laboratory (48%, 48%, 38%) and greenhouse (100%, 85%, 90%) settings. Q12 demonstrated better control against bacterial pathogens in vivo (approximately 50%). Whole-genome sequencing of Q12 and US1 revealed a genome size of approximately 4.1 Mb. Q12 had 4413 gene IDs and 4300 coding sequences, while US1 had 4369 gene IDs and 4255 coding sequences. Q12 exhibited a higher number of genes classified under functional subcategories related to stress response, cell wall, capsule, levansucrase synthesis, and polysaccharide metabolism. Both Q12 and US1 contained eleven secondary metabolite gene clusters as identified by the antiSMASH and RAST servers. Notably, Q12 possessed the antibacterial locillomycin and iturin A gene clusters, which were absent in US1. This genetic information suggests that Q12 may have a more pronounced control over bacterial pathogens compared to US1. Metabolic profiling of the superior strains, as determined by LC/MS/MS, validated our genetic findings. The investigated strains produced compounds such as iturin A, bacillomycin D, surfactin, fengycin, phenazine derivatives, etc. These compounds reduced spore production and caused deformation of the hyphae in F. solani. In contrast, B. velezensis UR1, which lacked the production of surfactin, fengycin, and iturin, did not affect these structures and failed to inhibit the growth of any pathogens. Our findings suggest that locillomycin and iturin A may contribute to the enhanced control of bacterial pectolytic rot by Q12. [ABSTRACT FROM AUTHOR]

Plant diseases significantly impact agricultural productivity. Early disease identification and diagnosis are critical for plant protection. Recent deep learning approaches have substantially aided detection of plant diseases. However, existing plant leaf disease identification techniques do not provide sufficient disease detection accuracies when the input image resolution is low. Images of grape leaves taken in the field may be low-resolution (LR) in nature due to limited lighting and varying weather conditions. Such LR images may result in incorrect real-time disease diagnosis. Hence super-resolution is a way to solve this problem. This research work proposes a novel Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD) in the Grape plant. The input LR image is separated into two subcomponents using the guided filtering technique. Features from the two subcomponents are extracted using single convolutional layer and four layers of the two-channel residual skip network. Concatenation is used to combine the features of two channels. Following this, a decoding block and a convolutional layer are utilized to generate the super-resolution (SR) image. A new collaborative loss function is proposed for training. The obtained SR image is given to the Disease Detection Network (DDN) for grape leaf disease detection. This approach, based on an LR image, assists the farmer in spotting grape plant diseases very early. Proposed model was extensively trained and tested on PlantVillage, Grape 400, Grape Leaf Disease datasets with multiple super-resolution scaling factors for various grape leaves images. For different super-resolution scaling factors such as Χ2, Χ4, Χ6, the proposed model RSNSR-LDD achieved accuracies of 97.19%, 99.37%, and 99.06% for the PlantVillage dataset, 96.88%, 97.12%, and 95.43% for the Grape400 dataset, and 100% for the Grape Leaf Disease dataset. [ABSTRACT FROM AUTHOR]

In 2019, brown root rot symptoms were observed in pea sprouts (Pisum sativum L.) grown in a plant factory in Japan. Bacteria were isolated from symptomatic plants and confirmed to cause the same symptoms in inoculation to pea seedlings. Based on multilocus sequence analysis and the bacteriological characteristics, the isolates were identified as a Pseudomonas species belonging to the Pseudomonas asplenii subgroup, which is most closely related to Pseudomonas vanderleydeniana. To our knowledge, this is the first report of a bacterial root disease in pea plants and of a P. asplenii subgroup member that is pathogenic to legumes. [ABSTRACT FROM AUTHOR]

Plant Prot. 2023; 77: 31-35 16 Yoshida S, Tsushima S (2020) Development of a novel soil-borne disease management strategy, health checkup based soil-borne disease management (HeSoDiM). Our developed soil-borne disease management system, HeSoDiM, is anticipated to contribute to attaining the sustainable use of arable lands by preventing soil-borne diseases, and its dissemination contributes to the promotion of agriculture. An extension worker evaluation found disease management against club root of broccoli based on HeSo + to be successful, and the application was a useful tool for farmers to discuss and adopting disease management strategies. [Extracted from the article]

Key message: The novel interkingdom PGPM consortia enhanced the ability of plant growth promotion and disease resistance, which would be beneficial to improve plant growth in sustainable agriculture through engineering microbiome. Plant growth-promoting microbes (PGPMs) play important roles in promoting plant growth and bio-controlling of pathogens. Much information reveals that the plant growth-promoting ability of individual PGPM affects plant growth. However, the effects of the PGPM consortia properties on plant growth remain largely unexplored. Here, we characterized three new PGPM strains including Rhodotorula graminis JJ10.1 (termed as J), Pseudomonas psychrotolerans YY7 (termed as Y) and P. chlororaphis T8 (termed as T), and assessed their effects in combination with Bacillus amyloliquefaciens FZB42 (termed as F) on plant growth promotion and disease prevention in Arabidopsis thaliana and tomato (Solanum lycopersicum) plants by investigating morphological changes, whole-genome sequencing and plant growth promoting (PGP) characterization. Results revealed that the three new strains R. graminis JJ10.1, P. psychrotolerans YY7 and P. chlororaphis T8 had the potential for being combined with B. amyloliquefaciens FZB42 to form interkingdom PGPM consortia. The combinations of R. graminis JJ10.1, B. amyloliquefaciens FZB42, and P. psychrotolerans YY7, i. e. JF and JYF, exhibited the strongest ability of synergetic biofilm production. Furthermore, the growth-promotion abilities of the consortia were significantly enhanced compared with those of individual strains under both inoculation and volatile organic compounds (VOCs) treatment. Importantly, the consortia showed stronger abilities of in planta disease prevention than individual strains. Findings of our study may provide future guidance for engineering the minimal microbiome communities to improve plant growth and/or disease resistance in sustainable agriculture. [ABSTRACT FROM AUTHOR]

Crop diseases can have a detrimental impact on crop growth, resulting in a significant reduction in crop yield. Therefore, accurate detection of these diseases is crucial for enhancing crop productivity. Despite notable advancements in deep learning techniques for disease identification, most experiments have been conducted under simplified laboratory conditions, posing challenges for accurately identifying crop diseases in complex real-world field environments. To bridge this gap, we draw inspiration from the Transformer model's ability to capture long-range global dependencies and handle occlusion. We propose a novel approach called Dense CNNs and Transformer Network (DCTN) for accurate detection of field crop diseases. Moreover, we introduce a new attention mechanism that utilizes multi-head self-attention via deep separable convolution projection and down-sampling, significantly enhancing computational efficiency. Additionally, we have meticulously curated and cleaned a dataset of 45,547 images depicting healthy and diseased crops in real-field environments. Our proposed method demonstrates superior performance, particularly in terms of its robustness against background interference in crop disease detection. Notably, DCTN achieves accuracies of 93.01% and 99.69% on our dataset and a publicly available dataset, respectively. For those who are interested, the code for our approach will be made available on https://github.com/wh9704/DCTN. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers' incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers. [ABSTRACT FROM AUTHOR]