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Phenological shifts in wild-growing plants and wild animal phenophases are well documented at many European sites. Less is known about phenological shifts in agricultural plants and how wild ecosystem phenology interacts with crop phenology. Here, we present long-term phenological observations (1961–2021) from the Czech Republic for wild plants and agricultural crops and how the timing of phenophases differs from each other. The phenology of wild-growing plants was observed at various experimental sites with no agriculture or forestry management within the Czech Hydrometeorological Institute observations. The phenological data of the crops were collected from small experimental plots at the Central Institute for Supervising and Testing in Agriculture. The data clearly show a tendency to shift to earlier times during the observation period. The data also show some asynchrony in phenological shifts. Compared with wild plants, agricultural crops showed more expressive shifts to the start of the season. Phenological trends for crop plants (Triticum aestivum) showed accelerated shifts of 4.1 and 5.1 days per decade at low and middle altitudes, respectively; on the other hand, the average phenological shift for wild plants showed smaller shifts of 2.7 and 2.9 days per decade at low and middle altitudes, respectively. The phenophase ´heading´ of T. aestivum showed the highest correlation with maximum temperatures (r = 0.9), followed by wild species (with r = 0.7–0.8) and two remaining phenophases of T. aestivum jointing and ripening (with r = 0.7 and 0.6). To better understand the impacts of climate on phenological changes, it is optimal to evaluate natural and unaffected plant responses in wild species since the phenology of field crops is most probably influenced not only by climate but also by agricultural management. [ABSTRACT FROM AUTHOR]

Ageratum conyzoides is a very aggressive and fast-spreading weed plant of great economic importance. Chemical herbicides have been used to control this weed, but this method has contributed to health hazards and environmental pollution. The present study evaluated the potency and safety of metabolites produced by rhizospheric fungi on the post-emergence of A. conyzoides. Fungi were isolated, and identified by phenotypic and molecular methods. The in-vitro herbicidal activity of the fungal isolates was carried out using a detached leaf necrosis assay. The secondary metabolites produced by the fungal strains were tested on post-emergence of A. conyzoides plants both in the green house and field. The fungal isolates from the rhizosphere were identified as belonging to the genera Aspergillus, Trichophyton, Fusarium, Penicillium, Mucor, Alternaria, Paecilomyces, Rhizoctonia, and Rhizopus. The in-vitro herbicidal assay revealed that 40% of the isolates induced necrotic activity. The metabolites produced by Fusarium oxysporum RF2, Alternaria alternata RF3 and F. incarnatum-equiseti RF4 showed stronger phytotoxic activities on the post-emergence of A. conyzoides in the greenhouse (70 – 90%) and field (86.5 – 100%) experiments. The safety experiments revealed that the metabolites had no toxic effect on soil microbes and crop plants. The findings of this study suggest that the metabolites from Fusarium oxysporum RF2, F. incarnatum-equiseti RF4, and Alternaria alternata RF3 could be explored as potential post-emergent biological agents for Ageratum conyzoides control on fields. [ABSTRACT FROM AUTHOR]

Plant roots are crucial for nitrogen uptake. To efficiently acquire N, root system architecture (RSA), which includes the length and quantity of primary roots, lateral roots and root hairs, is dynamically regulated by the surrounding N status. For crops, an ideotype RSA characterized by enhanced plasticity to meet various N demands under fluctuating N conditions is fundamental for high N utilization and subsequent yield. Therefore, exploring the genetic basis of N-dependent RSA, especially in crops, is of great significance. This review summarizes how plants sense both local and systemic N signals and transduce them to downstream pathways. Additionally, it presents the current understanding of genetic basis of N-dependent root plasticity in Arabidopsis and major crops. Also, to fully understand the mechanisms underlying N-dependent root morphogenesis and effectively identify loci associated with an ideotype RSA in crops, more attention should be paid to non-destructive, in situ phenotyping of root traits, cell-type-specific exploration of gene functions, and crosstalk between root architecture, environment and management in the future. [ABSTRACT FROM AUTHOR]

Climate change and various global crises have introduced significant challenges to food security. The agricultural sector faces the consequences of extreme weather conditions, such as heatwaves, droughts, floods, and other unpredictable events that impact crop yields and pose threats to both plants and animals. The loss of biodiversity and destruction of natural habitats crucial for agriculture limit options for adaptation in changing conditions. Pollution of soil, water, and air further jeopardizes the quality and safety of food, while excessive use of chemical fertilizers and pesticides raises concerns for human health and ecosystems. The issues of global overpopulation and overconsumption strain natural resources like land, water, and energy, making it increasingly difficult to produce enough food. Urbanization and infrastructure development further diminish available agricultural land, exacerbating food security concerns. Collectively, these factors raise profound questions about food security in many parts of the world, necessitating innovative solutions. Amid these challenges, the utilization of technological innovations, such as seed priming, stands as a relevant and crucial component of addressing agricultural problems and enhancing food security. Seed priming is an essential agrotechnical practice in which seeds undergo treatment with various factors, including water, chemical solutions, and physical influences like magnetic fields or UV irradiation, before sowing. These processes enhance seed quality, stress resistance, and efficiency in changing agro-climatic conditions, ultimately contributing to better crop growth outcomes. The modern trend in agriculture is shifting away from traditional chemical compounds towards physical methods. This transition is driven by the need for innovative, environmentally friendly, and economically competitive techniques within the seed industry. Biotechnological and molecular tools, products, and treatments play a vital role in advancing the seed industry by improving seed quality, crop yields, and reducing environmental impact. Physical methods for seed invigoration offer numerous advantages over chemical treatments. They can reduce the dependency on fertilizers, consequently reducing contamination of raw materials produced on farms. Additionally, physical methods can disinfect seeds during storage and before sowing. The impact of physical treatments on seed invigoration is influenced by factors such as the type and dose of magnetic fields or radiation, as well as plant characteristics, including cultivar, species, age, ploidy, and the complexity of the target tissue or organ. Recent research in toxicology has introduced the concept of hormesis, which involves using minimal toxin concentrations for stimulation. Reports suggest that seed irradiation can stimulate germination, photosynthetic pigment production, and plant growth, though prolonged exposure may have detrimental effects. Seed priming, primarily, is a well-established treatment that temporarily stimulates pre-germinative metabolism, activating processes related to DNA repair and antioxidant functions. It effectively enhances seed germination, vigor, field establishment, and seed growth under adverse environmental conditions. Techniques like osmo-priming and hydropriming have shown promise in rehabilitating low-vigor seeds. In the field of seed technology, physical methods have demonstrated greater effectiveness than traditional methods. One of the new physical methods of pre-sowing seed treatment is the use of ultraviolet irradiation and especially the use of UV-C irradiation. This method is usually non-invasive, cost-effective and environmentally friendly. In this work we reviewed the use of physical seed treatment methods, highlighting their potential advantages and disadvantages, and discussed the prospects of these methods to meet the needs of consumers, and seed producers. [ABSTRACT FROM AUTHOR]

The effective approach to environmentally friendly weed control through allelopathy was reviewed. Food security and agricultural sustainability are threatened by the growing world population. In contemporary agriculture, allelopathy has become a practical method for addressing a variety of problems such weed management. The use of allelopathy for weed and other pest management in agricultural production is being investigated using a number of techniques, including as crop rotation, cover cropping, intercropping, mulching, crop residue integration, and water or ethanol extract application. Allelopathic effects for sustainable weed management can be manifested in the form of weed on crop, crop on weed, and weed on weed. Different types of crude extract or ethanol/methanol extracts of either the leaf, stem, rhizomes, or flowers of the concerned species have been shown to have an inhibitory effect on both crop and weed species biomass production as well as seed germination, radicle and plumule elongation. Allelopathy, one of several weed management strategies, may result in lower labor costs and more efficiency without harming the environment. Alternatively, allelopathy can be utilized in the form of extracts of a promising crop or weed species to suppress weed populations with a reduced herbicide rate, thereby broadening the spectrum of control measures. [ABSTRACT FROM AUTHOR]

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Mechanical stimulation, including wind exposure, is a common environmental factor for plants and can significantly impact plant phenotype, development, and growth. Most responses to external mechanical stimulation are defined by the term thigmomorphogenesis. While these morphogenetic changes in growth and development may not be immediately apparent, their end-results can be substantial. Although mostly studied in dicotyledonous plants, recently monocot grasses, particularly cereal crops, have received more attention. This review summarizes current knowledge on mechanical stimulation in plants, particularly focusing on the molecular, physiological, and phenological responses in cereals, and explores practical applications to sustainably improve the resilience of agricultural crops. [ABSTRACT FROM AUTHOR]

AbstractDue to climate change, abiotic stresses have arisen and are harming agricultural water management and agricultural productivity worldwide. To address this issue, bio-stimulators have been explored to improve water management and nutrient use efficiency, reflecting improved agricultural productivity due to suppression of climate change-induced stresses and the improved plant stress tolerance. Bio-stimulators play a dual role as they increase tolerance to abiotic stresses and feed the plant to adapt to stress and survive. Maize grain embryo extract (MGEE) and diluted bee-honey solution (DBHS) are a novel group of bio-stimulators recently used, beginning in 2014, for some crop plants (common bean, faba bean, sunflower, wheat, maize, onion, atriplex, roselle, and chili pepper) grown under certain abiotic stressors (salinity, drought, cadmium, nutrient deficiency, and chemical fertilizers). Reports signalized that MGEE and DBHS effectively induce improvements in molecular and morpho-physio-biochemical indices and positive stimulation of antioxidant defense systems, all of which enable plants to cope with stress-stimulated toxicity and maintain basic metabolic capacity under abiotic stress. Under the above-mentioned stresses, MGEE and DBHS can provide efficient mechanisms to enhance plant growth and yield traits, yield quality characteristics, photosynthetic efficiency, low-molecular-weight antioxidants, nutrient and hormonal homeostasis, antioxidant redox states, enzymatic antioxidant activities, antioxidant and polyamine gene expressions, etc. Extensive research into the potential of MGEE and DBHS to sustain plant performance under adverse field conditions would strengthen their potential as a cheaper and eco-friendly alternative to harmful and high-cost chemicals. [ABSTRACT FROM AUTHOR]

Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low coverage, there is limited investigation into the later stages of high coverage, which could impact the accuracy of forecasting yield. High canopy coverage and severe occlusion in later stages pose significant challenges for plant detection and counting. Therefore, this study evaluated the performance of the Count Crops tool and a deep learning (DL) model derived from early-stage unmanned aerial vehicle (UAV) imagery in detecting and counting Konjac plants during the high-coverage growth stage. Additionally, the study proposed an approach that integrates the DL model with Konjac location information from both early-stage and high canopy coverage stage imagery to improve the accuracy of recognizing Konjac plants during the high canopy coverage stage. The results indicated that the Count Crops tool outperformed the DL model constructed solely from early-stage imagery in detecting and counting Konjac plants during the high-coverage period. However, given the single stem and erect growth characteristics of Konjac, incorporating the DL model with the location information of the Konjac plants achieved the highest accuracy (Precision = 98.7%, Recall = 86.7%, F1-score = 92.3%). Our findings indicate that combining DL detection results from the early growth stages of Konjac, along with plant positional information from both growth stages, not only significantly improved the accuracy of detecting and counting plants but also saved time on annotating and training DL samples in the later stages. This study introduces an innovative approach for detecting and counting Konjac plants during high-coverage periods, providing a new perspective for recognizing and counting other crop plants at high-overlapping growth stages. [ABSTRACT FROM AUTHOR]

Agroforestry is increasingly recognized as an effective tool for enhancing multifunctionality in agroecosystems globally, improving land-use efficiency and delivering multiple ecosystem services (ES). This study investigates the multifunctionality of tree-based intercropping (TBI) systems, which integrate widely spaced rows of trees with agricultural crops and can be adapted to different climates. We assessed spatial gradients of 11 ES indicators based on field measurements taken at increasing distances from the tree rows within a temperate TBI system of 50 trees ha-1, aged 7 to 10 years. These indicators were compared between the TBI system and agricultural control plots, which were managed similarly to the cultivated alleys in the TBI system but without trees. We measured light availability, soil moisture and crop yields (forage and wheat) across cultivated alleys associated with three tree species compositions: 1) red oak in monoculture, 2) hybrid poplar in monoculture, and 3) a mix of red oak and hybrid poplar alternating along the row. The greatest variation in ES indicators within the cultivated alleys, compared to the agricultural controls, was frequently observed nearest to the tree rows. Specifically, yields of annual crops (wheat and corn), soil moisture, P supply, NO3- leaching rates and bulk density decreased, while potential evaporation increased in areas closest to the tree rows, in contrast to measurements taken near the alley centre and in the control plots. Other ES indicators, including forage yield, N and K supplies, and soil C stocks, remained unaffected by the TBI system. Our results suggest that trade-offs between ES may occur at fine scales and be location-specific within TBI systems. Plots containing poplar (alone or mixed with red oak) exhibited lower soil moisture and light availability compared to those with red oak only, resulting in a greater decrease in wheat yield at the tree-crop interface. Conversely, C stocks in fast-growing poplar biomass were substantially higher than those in red oak. We conclude that composition of tree species is crucial in determining trade-offs in ES delivery within TBI systems. At the system level, we found comparable levels of multifunctionality between TBI and control plots, likely due to the limited sample size of aggregated data. [ABSTRACT FROM AUTHOR]

Clubroot disease is an important disease of cruciferous crops worldwide caused by Plasmodiophora brassicae. The pathogen P. brassicae can infect almost all cruciferous crops, resulting in a reduction in yield and quality of the host plant. The first part of this review outlines the process of P. brassicae infestation, effectors, physiological pathotypes and identification systems. The latter part highlights and summarizes the various current control measures and research progress on clubroot. Finally, we propose a strategic concept for the sustainable management of clubroot. In conclusion, this paper will help to deepen the knowledge of P. brassicae and the understanding of integrated control measures for clubroot, and to lay a solid foundation for the sustainable management of clubroot. [ABSTRACT FROM AUTHOR]

Introduction: Specific microorganisms and metabolites in soil play key roles in regulating organismal behavior. Currently, the effects of different preceding crops on the rhizosphere soil quality of flue-cured tobacco remain unclear. Methods: Four treatments were compared in the study: fallow + tobacco (CK), maize + tobacco (T1), rapeseed + tobacco (T2), and wheat + tobacco (T3). Results and discussion: Results showed that preceding crops significantly enhanced soil nutrient levels and improved tobacco growth by altering rhizosphere metabolites and microbial community structure. Previous cultivation of maize and rapeseed significantly promoted tobacco growth, rapeseed and wheat cultivation enhanced the diversity of soil bacterial communities, and notably decreased the abundance of urea-degrading bacteria. In contrast, the preceding crop of maize reduced plant pathogenic fungi and promoted positive microbial interactions. Metabolomics analysis showed that different preceding crops altered lipids, organic acids, flavonoids, alkaloids, and terpenoids, enhancing secondary metabolite synthesis pathways in soil. Preceding crops regulated rhizosphere metabolites which potentially participated in soil carbon and nitrogen cycling, balancing soil nutrients, and improving tobacco yield. Overall, the three preceding crops altered the composition and function of metabolites and microbial community structures in rhizosphere soil, thereby increased soil nutrient concentration. Both maize and rapeseed cultivation significantly boosted tobacco growth and biomass. These findings offer new insights into the potential interactions between rhizosphere metabolites and microbial communities and strategies of comprehensively regulating tobacco growth. [ABSTRACT FROM AUTHOR]

Climate change is significantly impacting our agricultural crops and their cultivation areas, which are expected to change considerably by the end of the century. Temperature conditions decisively influence the safe suitability of grapes in a given location. To address these changes, we analysed the temporal changes of four temperature indicators: Average Growing Season Temperature (AGST), Growing Degree Days (GDD or Winkler index (GDD-WI), Huglin index (HI), and Biologically Effective Degree Days (BEDD) across 22 Hungarian wine regions from 1971 to 2100. The analysis was based on data from 14 climate models under RCP 4.5 and RCP 8.5 scenarios. To investigate the future suitability of wine grapes, we introduced the dynamic suitability function, which allowed us to analyse the suitability of the average temperature during the growing season for 21 wine grape varieties from 2031 to 2100 in decadal increments. Additionally, a temperature impact function was introduced to characterise the suitability of 21 wine grape varieties with values ranging from 0 to 1, based on the average temperature during the growing season. The results confirmed that the frequency of temperature indices used in grape cultivation will shift distinctly towards warmer climate classes in the future. The increasingly warmer climate presents certain advantages but also has growing cultivation risks. In the most optimistic scenario, the average temperature during the growing season may decrease by 0.8°C over the next seven decades. However, in the most pessimistic model, the change expected by the end of the century exceeds a 4.0°C increase. For wine grape varieties with lower heat requirements, suitability under the pessimistic RCP 8.5 emission scenario is projected to decrease by 29% by the end of the century. Conversely, under the optimistic scenarios, the decline in suitability values is only between 3-4%. For grape varieties with higher heat requirements, a 10% decrease in suitability is expected under the RCP 8.5 scenario. In contrast, the RCP 4.5 scenario suggests that suitability could improve by 1-2% by the end of the century. These findings contribute to a better understanding of the impacts and consequences of climate change and offer insights on how to prepare for these challenges in the viticulture sector. [ABSTRACT FROM AUTHOR]

Accurate counting of crop plants is essential for agricultural science, particularly for yield forecasting, field management, and experimental studies. Traditional methods are labor-intensive and prone to errors. Unmanned Aerial Vehicle (UAV) technology offers a promising alternative; however, varying UAV altitudes can impact image quality, leading to blurred features and reduced accuracy in early maize seedling counts. To address these challenges, we developed RC-Dino, a deep learning methodology based on DINO, specifically designed to enhance the precision of seedling counts from UAV-acquired images. RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. The RSCconv layer improves the representation of early maize seedlings compared to non-seedling elements within feature maps by calibrating spatial domain features. The ASCFF module enhances the discriminability of early maize seedlings by adaptively fusing feature maps extracted from different layers of the backbone network. Additionally, transfer learning was employed to integrate pre-trained weights with RSCconv, facilitating faster convergence and improved accuracy. The efficacy of our approach was validated using the Early Maize Seedlings Dataset (EMSD), comprising 1,233 annotated images of early maize seedlings, totaling 83,404 individual annotations. Testing on this dataset demonstrated that RC-Dino outperformed existing models, including DINO, Faster R-CNN, RetinaNet, YOLOX, and Deformable DETR. Specifically, RC-Dino achieved improvements of 16.29% in Average Precision (AP) and 8.19% in Recall compared to the DINO model. Our method also exhibited superior coefficient of determination (R²) values across different datasets for seedling counting. By integrating RSCconv and ASCFF into other detection frameworks such as Faster R-CNN, RetinaNet, and Deformable DETR, we observed enhanced detection and counting accuracy, further validating the effectiveness of our proposed method. These advancements make RC-Dino particularly suitable for accurate early maize seedling counting in the field. The source code for RSCconv and ASCFF is publicly available at https://github.com/collapser-AI/RC-Dino , promoting further research and practical applications. [ABSTRACT FROM AUTHOR]

AbstractThe impacts of salt, drought, and waterlogging are serious challenges for agriculture as they can adversely impact crop development, yield, and overall quality. The study aims to examine morphological, yield, and physiological responses of crop plants under salt, drought, and waterlogging stress and the management strategies by exogenous application to increase crop tolerance and productivity under these stressful conditions. Morphological characteristics and yield of crop plants are significantly affected by these stresses. Crop plants adapt their roots, branches, leaves, and reproductive organs to survive and grow under these stresses. Therefore, inducing tolerance in plants to salinity, drought, and waterlogging stress is crucial for sustainable agriculture. Various approaches, including the exogenous application of hormones such as auxins and melatonin, as well as the use of biochar, have revealed promising results in mitigating the adverse effects of these stresses. [ABSTRACT FROM AUTHOR]

Root rot is a general term for soil-borne diseases that cause the necrosis and decay of underground plant parts. It has a wide host range and occurs in various types of plants, including crops, horticultural crops and medicinal plants. Due to the fact that medicinal plants generally have a long growth cycle and are primarily the root and rhizome herbs. This results in root rot causing more serious damage in medicinal plant cultivation than in other plants. Infected medicinal plants have shrivel or yellowed leaves, rotting rhizomes, and even death of the entire plant, resulting in a sharp decline in yield or even total crop failure, but also seriously reduce the commercial specifications and effective ingredient content of medicinal plants. The pathogens of root rot are complex and diverse, and Fusarium fungi have been reported as the most widespread pathogen. With the expansion of medicinal plant cultivation, root rot has occurred frequently in many medicinal plants such as Araliaceae, Fabaceae, Ranunculaceae, and Solanaceae and other medicinal plants. This article reviews recent research progress on root rot in medicinal plants, covering various aspects such as disease characteristics, occurrence, pathogen species, damage to medicinal plants, disease mechanisms, control measures, and genetic factors. The aim is to provide reference for better control of root rot of medicinal plants. [ABSTRACT FROM AUTHOR]

Accurate information on crop planting and spatial distribution is critical for understanding and tracking long-term land use changes. The method of using deep learning (DL) to extract crop information has been applied in large-scale datasets and plain areas. However, current crop classification methods face some challenges, such as poor image time continuity, difficult data acquisition, rugged terrain, fragmented plots, and diverse planting conditions in complex scenes. In this study, we propose the Complex Scene Crop Classification U-Net (CSCCU), which aims to improve the mapping accuracy of staple crops in complex scenes by combining multi-spectral bands with spectral features. CSCCU features a dual-branch structure: the main branch concentrates on image feature extraction, while the auxiliary branch focuses on spectral features. In our method, we use the hierarchical feature-level fusion mechanism. Through the hierarchical feature fusion of the shallow feature fusion module (SFF) and the deep feature fusion module (DFF), feature learning is optimized and model performance is improved. We conducted experiments using GaoFen-2 (GF-2) images in Xiuwen County, Guizhou Province, China, and established a dataset consisting of 1000 image patches of size 256, covering seven categories. In our method, the corn and rice accuracies are 89.72% and 88.61%, and the mean intersection over union (mIoU) is 85.61%, which is higher than the compared models (U-Net, SegNet, and DeepLabv3+). Our method provides a novel solution for the classification of staple crops in complex scenes using high-resolution images, which can help to obtain accurate information on staple crops in larger regions in the future. [ABSTRACT FROM AUTHOR]

A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 h –14:00 h) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network. [ABSTRACT FROM AUTHOR]

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research's significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future. [ABSTRACT FROM AUTHOR]

Agriculture and stockbreeding have inspired the invention of the Saint Lubin Festival (Fête de la Saint-Lubin) in the town of Rambouillet, France. This paper describes the Festival, while also placing it in a wider cultural, socio-economic, and political context. We can propose that this event involves ‘performing’ agriculture and that it can be fruitfully compared with some of the ways people work with crops and animals in agricultural museums, living history sites or heritage events, where the true constraints of farming also do not apply. All of these events have educational or heritage aims and often attempt to connect urban populations with their rural food-producers. How effective they are at the latter might well be debated, but they draw us into a wider debate about heritage and food production, with a song thrown in, for spice. [ABSTRACT FROM AUTHOR]

Background: The use of chemical fertilizers and pesticides and the farming without crop rotation may negatively impact the microbial community and the quality of the soils in ginger farm. It is important to improve soil properties to promote the healthy growth of ginger in ginger farm. Results: We isolated and identified the pathogenic Fusarium ramigenum from infected ginger roots. We then isolated a new Bacillus paralicheniformis strain SYN-191 from the rhizosphere soil around healthy ginger roots, and showed B. paralicheniformis SYN-91 could inhibit F. ramigenum growth, degrade proteins, dissolve silicate, and decompose cellulose. SYN-191 treatment significantly improved the agronomic traits of ginger seedlings in healthy soil and continuous cropping soil. Furthermore, SYN-191 treatment restructured the microbial microbiomes in rhizosphere soil, including reducing the number of harmful fungi, such as Fusarium, and increasing the beneficial bacterial populations such as Bacillus and Pseudomonas. Field experiments showed that SYN-191 application increased ginger yield by 26.47% (P < 0.01). Whole-genome sequencing of strain SYN-191 revealed the relevant genes for antibiotic synthesis, potassium dissolution, and cellulose decomposition. Conclusions: A new plant-growth-promoting B. paralicheniformis SYN-191 was obtained. This strain could antagonize ginger root rot pathogenic fungus, improve agronomic traits and ginger yield in field, and improve the microbial community structure in the ginger rhizosphere soil. This study provides a valuable bacterial resource for overcoming obstacles in the continuous cropping of ginger. [ABSTRACT FROM AUTHOR]

Sargassum polycystum is among the seaweed species in the Philippines that are considered coastal waste after drifting on the shorelines. Although there has been a report on the potential of S. polycystum as a biostimulant for certain crops, its utilization as a hydroponic nutrient solution is not yet thoroughly analyzed. Thus, a study utilizes various ratios of S. polycystum seaweed extract (SE) mixed with commercial hydroponic nutrient solution (CHNS) to the growth and yield of green leaf lettuce grown under the Kratky method of hydroponic system. Treatments are 100% CHNS and percentage replacements of CHNS with SE (25%, 50%, and 75%, respectively). The result shows a 10.53% increase in lettuce plant height grown under 25% and 50% SE compared to 100% CHNS. A similar result is also observed in the lettuce leaf width (14.29%), leaf length (7.14%), and plant weight (22.41%). Regarding root length, replacing 50% SE resulted in longer root length (44.83%) and more leaves (10%) compared to lettuce plants grown under 100% CHNS. However, replacement of SE beyond 50% is detrimental to leaf length and comparable to all growth and yield parameters with 100% CHNS. Regarding the return on investment, 50% SE replacement has the highest return of 98.62% compared to 100% CHNS, with 49.87% only for a projected six cropping cycles. Hence, a 50% replacement of CHNS with SE from its recommended rate can be suggested for more profitable production of green leaf lettuce under a hydroponic system. [ABSTRACT FROM AUTHOR]

The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings. [ABSTRACT FROM AUTHOR]

Looper moths of the genus Chrysodeixis (Lepidoptera: Noctuidae: Plusiinae) are important pests of many crops and native plants worldwide. Chrysodeixis chalcites (Esper) is listed as an invasive species for the United States with records of interception. Native species of the Plusiinae subfamily are morphologically similar and commonly cross-attracted in survey trapping programs for C. chalcites , such as Chrysodeixis includens (Walker), a native economic pest. The species identification relies on male genitalia dissection and DNA analysis. These processes are time and cost-consuming and require expertise. In this work, we evaluated the use of wing geometric morphometrics (GM) as a tool to overcome the identification challenges associated with the complex morphologies of Chrysodeixis spp. The cleaned wings of specimens with validated identification were photographed under a digital microscope, and seven venation landmarks were annotated from the images. The digital coordinates of the wing landmarks were analyzed in MorphoJ. Our results validated the use of GM for distinguishing the invasive C. chalcites from the native C. includens. A limited number of landmarks on the center of the wing was used to address the challenges in GM for trap-collected lepidopteran pests. Future automation of the novel application of GM for identifying C. includens can be explored in trapping systems for IPM and surveys for the invasive C. chalcites. [ABSTRACT FROM AUTHOR]