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Industrialized seeding production is one of the most important methods used to raise seedlings in a standardized and efficient manner. However, its consumption of light energy limits its development. In this paper, we studied the effects of LED light intensities (30 (L1), 60 (L2), 90 (L3), 120 (L4), 150 (L5), 180 (L6), and 210 (L7) µmol·m−2·s−1) on the phenotypic parameters and health index of seedlings during different growth stages (cotyledon flattening (CF), one bud and one leaf (OBOL), and one bud and two leaves (OBTL)). Ten out of sixteen plants were selected to measure the parameters in every treatment. The control group was treated with 150 µmol·m−2·s−1 all the time. The results show that the height of the L1-treated plants was greatest during the cotyledon flattening stage. The L7 treatment produced the largest stem diameter, the greatest biomass, the largest leaf area, and the largest leaf circumference, the values of which were 2.61 mm, 3.3 g, 60.67 cm2, and 39.71 cm, respectively. During the one bud and one leaf stage, the L1 treatment produced the tallest plants. The L7 treatment resulted in the largest stem diameter, the greatest biomass, the largest leaf area, and the largest leaf circumference. During the one bud and two leaves stage, the plant height, stem diameter, leaf circumference, and biomass produced of the L6 treatment were the greatest, with values of 68.37 mm, 3.09 mm, 51.54 cm, and 4.47 g. L5 resulted in the largest leaf area (63.91 cm2). The seedling index of the three stages showed an inflection point at the L4 level. The experimental results showed that the light intensity required by the cucumber seedlings was 120 µmol·m−2·s−1, 150 µmol·m−2·s−1, and 120 µmol·m−2·s−1 during the three stages, from the cotyledon flattening stage to the one bud and two leaves stage. The results provide references regarding the light required during the cultivation of cucumber seedlings. [ABSTRACT FROM AUTHOR]

Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth. [ABSTRACT FROM AUTHOR]

The world's population is expected to increase by nearly two billion in the next 30 years; the population will increase from 8 billion to 9.7 billion by 2050 and could peak at 10.4 billion by the mid-2080s. The extreme weather triggered by global climate change has severely hit crop yields in open-field cultivation and led to an increase in food prices. Furthermore, in the last few years, emergency events such as the COVID-19 pandemic, wars/conflicts, and economic downturns have conditioned agricultural production and food security around the world. Greenhouses could be efficient cultivation systems because they enable food production in a sustainable way, limiting contact between pollutants and plants and optimizing the use of water, energy, and soil. This paper proposes a novel dome-soilless greenhouse concept for tomato cultivation in the Mediterranean area. The proposed greenhouse is fixed on a sea platform to take advantage of the seawater cooling environment and to integrate water consumption into a hydroponic system. In order to evaluate the best covering solution material to adopt, a few thermal and photometric characteristics of greenhouse covering materials were evaluated using a simplified method. A dynamic simulation was carried out to compare the proposed seawater cooling system with a conventional cooling tower in terms of the electric energy spent to maintain the inside temperature range at 13–25 °C across all seasons in the year. The proposed heating, ventilation, and air conditioning (HVAC) system allowed a total annual energy saving of more than 10%. The energy saved was a result of the better cooling performance of the seawater heat exchange that allowed energy saving of about 14% on cooling. The comparison between the model characterised by a 6 mm polycarbonate coupled with UbiGro film and a seawater cooling system, and the model including a 6 mm polycarbonate coupled with a clarix blue film covering and a tower cooling system highlighted energy saving of about 20%. The obtained results indicate possible future directions for offshore greenhouses to carry out independent production together with the integration of photovoltaic modules, water treatment plants, and smart remote-control systems. [ABSTRACT FROM AUTHOR]

Insulated plastic greenhouses (IPG) were a new type of facility emerging in production in China. The mechanism of construct parameters on the indoor solar radiation deserves further study. A mathematical model that can well describe the spatial and temporal distribution of solar radiation in the greenhouse was established. Based on this model, the effects of multiple construct parameters, such as insulation blanket shading, height–span ratio, greenhouse azimuth, and geographical latitude, on the indoor solar radiation of IPG were quantitatively specified. The results showed that the spatial and temporal distribution of inside solar radiation was highly variable along the span direction of IPG. And by analyzing the simulation data, it was found that for different heights of greenhouses, the corresponding optimal span is different. Therefore, in the construction of greenhouses, the appropriate height–span ratio should be selected according to the planting demand. And by simulating the inside solar radiation under different greenhouse azimuth angles, it was found that accumulated daily solar radiation in the greenhouse reaches the optimum value when the greenhouse azimuth angle is in the range of 0–20°. This paper can provide theoretical guidance for the design and optimization of structure parameters of IPG in China. [ABSTRACT FROM AUTHOR]

In recent years, an increase of interest has arisen in oats due to their unique health-related properties. Fusarium Head Blight (FHB) is recognized as a major threat to oat production and safety. The Joint FAO/WHO Expert Committee on Food Additives (JECFA) assesses the risks of the presence of Fusarium-produced mycotoxins in foods and the tolerable intake level. This paper summarizes updates on Fusarium resistance in oats, describing the advances in phenotyping strategies and diagnostics methods and discussing the role of the infection process of the microbiome and bioactive compounds peculiar to oats. A special emphasis has been placed on the presentation of new genetic, genomic, and biotechnological knowledge and tools available today and their perspectives on breeding programs aiming to develop FHB-resistant genotypes. [ABSTRACT FROM AUTHOR]

This paper clarifies the farm applicability and feasibility of spreading biodegradable plastic film mulching for garlic production to ensure the green and sustainable development of the garlic industry. We set up a field trial of garlic planting with biodegradable plastic film mulching (BM) and plastic film mulching (PM), using no film mulching (CK) as the control, and measured CH4 and N2O emissions in the garlic fields. The yield-scaled global warming potential, carbon footprint, and net ecosystem economic benefit (NEEB) were used to assess the comprehensive impact of the different treatments. Compared to the CK, film mulching significantly increased CH4 absorption, with significantly higher seasonal cumulative CH4 emissions (20.5%) in BM than PM, but significantly increased N2O emissions, with significantly lower seasonal cumulative N2O emissions (23.53%) in BM than PM. Both BM and PM improved garlic yield, with PM significantly increasing garlic yield by 18.86% compared to the CK. Moreover, film mulching significantly decreased the yield-scaled global warming potential (by 52.06% and 40.82% in PM and BM, respectively). PM had a significantly higher carbon footprint than BM. Film mulching improved NEEB by 9.29–11.78%. Considering crop yields and environmental benefits, we propose BM as an effective method for a green and efficient garlic production. [ABSTRACT FROM AUTHOR]

The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous insect pest monitoring rely on an internet connection and a power source. However, most cacao plantations are remotely located and have limited access to internet and power sources; therefore, a simpler and readily available tool is necessary to enable continuous monitoring. This research proposes a mobile application developed for rapid and on-site counting of CPBs on sticky paper traps. A CPB counting algorithm was developed and optimized to enable on-device computations despite memory constraints and limited capacity of low-end mobile phones. The proposed algorithm has an F1-score of 0.88, with no significant difference from expert counts (R2 = 0.97, p-value = 0.55, α = 0.05). The mobile application can be used to provide the required information for pest control methods on-demand and is also accessible for low-income farms. This is one of the first few works on enabling on-device processing for insect pest monitoring. [ABSTRACT FROM AUTHOR]

White lupine (Lupinus albus L.) is a well-known green manure crop in Hungary, but the production of seeds can be badly impacted by weeds. The sweet white lupine 'Nelly' was grown on acidic sandy soil, and experimental plots were treated with different herbicides. Flumioxazin (0.06 kg ha−1), pendimethalin (5 L ha−1), dimethenamid-P (1.4 L ha−1), pethoxamid (2 L ha−1), clomazone (0.2 L ha−1), metobromuron (3 L ha−1), and metribuzin (0.55 L ha−1) were applied pre-emergence (1–2 days after sowing). Imazamox was also tested and applied post-emergence (1 L ha−1) when some basal leaves were clearly distinct (BBCH 2.3). In this paper, the weed control efficiency and the phytotoxicity of herbicides applied to lupine are examined. Vegetation index datasets were collected 12 times using a manual device and 2 times using an unmanned aerial vehicle (UAV). The phytotoxicity caused by herbicides was visually assessed on several occasions throughout the breeding season. The frequency of weed occurrence per treatment was assessed. The harvested seed yields, in kg ha−1, were analyzed after the seeds were cleaned. The herbicides metribuzin and imazamox caused extensive damage to white lupine. While pendimethalin, dimethenamid-P, pethoxamid, and clomazone were outstanding in several measured indicators, the final ranking which summarizes all the variables showed that only the pethoxamid and clomazone treatments performed better than the control. Metribuzin and imazamox were highly phytotoxic to white lupine. In the future, it would be appropriate to integrate more post-emergence active substances into trials, and the pre-emergence herbicides involved in this study should be further tested. [ABSTRACT FROM AUTHOR]

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations. [ABSTRACT FROM AUTHOR]

To control heavy metal pollution effectively, prevention of heavy metal accumulations in agricultural soils should be the priority rather than remediation of heavy metal contamination. In this research, papers which contained input and output fluxes of Cd, As, Cr, Hg, and Pb in topsoil (the plough layer) of agricultural lands in Hunan, Zhejiang, the Yangtze River Delta, Hainan, and China as a whole were explored. Fluxes of heavy metal species were recalculated, and future trends of pollution were predicted. Also, cases regarding the application of technologies to control the input and output of heavy metals were analyzed. Results indicated that atmospheric decomposition was the dominant input source of heavy metals in all study sites except Hainan. The relative contributions of fertilizers, irrigation water, and straw returning fluctuated greatly among different sites. Cd pollution in all sites was the most serious, followed by Cr and Pb. In Hunan, Cd has already exceeded the maximum limit value and needs to be controlled urgently. The input of heavy metals from irrigation water, fertilizers, and straw returning could be controlled by proposing more policies to manage their quality and application amounts. The amount of heavy metals absorbed by plants could be increased by cultivating crops with hyperaccumulators and high-biomass plants. [ABSTRACT FROM AUTHOR]

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower's traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]

Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the "Wheat × Maize System". Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding. [ABSTRACT FROM AUTHOR]

Plants are vulnerable to many abiotic stresses, resulting in reduced plant productivity. Its adaptation to unfavorable environments relies on transmitting external stress signals into internal signaling pathways. A series of stress response mechanisms have been developed. Among them, brassinosteroids (BRs) are a class of steroid hormones that are widely involved in plant growth, development, and stress response. Via genetics, proteomics, and genomics studies, the major components of signaling and signaling pathways through a series of phosphorylation cascade reactions have been identified in model plants such as Arabidopsis. Numerous studies have shown that BRs play important roles in plant responses to drought, temperature, salt, heavy metals, and other environmental stresses. The application of BRs to improve plant stress resistance has become the focus of research in recent years, especially the regulation of stress via endogenous BRs. Therefore, this paper systematically summarizes the research progress related to endogenous BR levels and provides an overview of BR biosynthesis and the signaling pathway, as well as the function of endogenous BRs in the response to abiotic stresses. [ABSTRACT FROM AUTHOR]

Nanotechnology, nanosensors in particular, has increasingly drawn researchers' attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. [ABSTRACT FROM AUTHOR]

Macadamia decline poses a serious economic threat to the macadamia industry. It exhibits either a slow decline due to infection by Kretzschmaria clavus or Ganoderma lucidum, or a quick decline caused by pathogens like Phytophthora spp., Lasiodiplodia spp., Neofusiccocum spp., Nectria rugulosa, Xylaria arbuscula, Phellinus gilvus, Acremonium recifei, and Rosellinia spp. Chemical strategies, resistant cultivars, and agronomic measures have been widely adopted to control macadamia decline, but effective biological control measures have rarely been applied. This paper proposes two key steps for implementing biological control strategies, i.e., the isolation and selection of biological control agents from healthy plants, or from the disease-suppressive soil for the construction of synthetic microbial communities, and the integration of synthetic microbial communities with various strategies, including seed coating, root dipping, seedling substrate, soil drenching, foliar spraying, and application as a bio-organic fertilizer. By adopting these strategies, we aim to provide proactive and efficient approaches for combating macadamia decline and safeguarding the health of macadamia orchards. [ABSTRACT FROM AUTHOR]

This work was aimed at developing a prototype system based on multispectral digital photogrammetry to support tests required by international regulations for new Plant Protection Products (PPPs). In particular, the goal was to provide a system addressing the challenges of a new PPP evaluation with a higher degree of objectivity with respect to the current one, which relies on expert evaluations. The system uses Digital Photogrammetry, which is applied to multispectral acquisitions and Artificial Intelligence (AI). The goal of this paper is also to simplify the present screening process, moving it towards more objective and quantitative scores about phytotoxicity. The implementation of an opportunely trained AI model for phytotoxicity prediction aims to convert ordinary human visual observations, which are presently provided with a discrete scale (forbidding a variance analysis), into a continuous variable. The technical design addresses the need for a reduced dataset for training the AI model and relating discrete observations, as usually performed, to some proxy variables derived from the photogrammetric multispectral 3D model. To achieve this task, an appropriate photogrammetric multispectral system was designed. The system operates in multi-nadiral-view mode over a bench within a greenhouse exploiting an active system for lighting providing uniform and diffuse illumination. The whole system is intended to reduce the environmental variability of acquisitions tending to a standard situation. The methodology combines advanced image processing, image radiometric calibration, and machine learning techniques to predict the General Phytotoxicity percentage index (PHYGEN), a crucial measure of phytotoxicity. Results show that the system can generate reliable estimates of PHYGEN, compliant with existing accuracy standards (even from previous PPPs symptom severity models), using limited training datasets. The proposed solution addressing this challenge is the adoption of the Logistic Function with LASSO model regularization that has been shown to overcome the limitations of a small sample size (typical of new PPP trials). Additionally, it provides the estimate of a numerical continuous index (a percentage), which makes it possible to tackle the objectivity problem related to human visual evaluation that is presently based on an ordinal discrete scale. In our opinion, the proposed prototype system could have significant potential in improving the screening process for new PPPs. In fact, it works specifically for new PPPs screening and, despite this, it has an accuracy consistent with the one ordinarily accepted for human visual approaches. Additionally, it provides a higher degree of objectivity and repeatability. [ABSTRACT FROM AUTHOR]

Hydrothermal carbonization (HTC) is a proven cost-effective and energy-efficient method for waste management and value-added product recovery. There are, however, several issues that require further improvement or research. Identifying the strengths and weaknesses of HTC in comparison to traditional pyrolysis is crucial for scientists to choose between them or use both (complementary) to achieve specific product properties. Additionally, sharing information on diverse modeling approaches and scales is crucial to enhance the robustness and universality of HTC process models. In addition, the study on the applicability of hydrochars on target applications such as soil amendment is crucial to give back nutrients to soils and face the dependence on finite specific feedstocks in this field. Also, proper management of the process by-products, especially process water, must be addressed to improve the carbon and hydric footprint of the process. Reviewing the suitability of HTC to treat specific challenging wastes, whose strength is not related to their calorific value but to their nutrient composition (i.e., manures), is also an appealing topic for HTC research. This paper aims to tackle the above-mentioned issues through an updated review and discussion of research gaps that require further investigation. [ABSTRACT FROM AUTHOR]

The ecological niche gradient is an important determinant of microbial community structure. In this paper, we studied variation in rhizosphere bacterial diversity and community composition along an ecological niche gradient. We used the high-throughput sequencing of 16S rRNA genes to study changes in the rhizosphere soil microbial communities of six grass and four shrub species during the secondary succession of abandoned farmland on the Loess Plateau of China. A structural equation model (SEM) was employed to disentangle the relative contribution of ecological niche and soil properties to bacterial diversity and community composition. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla of rhizosphere bacteria in all samples. During the dynamics of the plant niche from low to high, bacterial community composition transitioned from Actinobacteria + Acidobacteria to Proteobacteria + Bacteroidetes higher abundance. Moreover, the bacterial diversity and species richness changed with an increasing niche gradient, showing a clear differentiation in the rhizosphere bacterial community of grassland and shrubland. Further, diversity and species richness decreased from the middle niche of B. ischaemum to the poles, indicating that the succession process had not yet reached the climax community stage. Community assembly analysis suggested that the stochastic process gradually strengthened along the increasing ecological niche gradient, especially the drift effect. Furthermore, SEM analysis showed that the ecological niche had significant negative effects on soil properties and bacterial richness, while the effects on bacterial diversity and the stochastic processes of community assembly were weakened and insignificant. Altogether, our findings suggest that the complex interaction of the ecological niche with bacterial diversity and composition was determined by soil properties. Further, bacterial diversity was not necessarily higher with increasing ecological niche gradients. [ABSTRACT FROM AUTHOR]

Genotype by environment interaction (GEI) is a complex problem that complicates the barley selection and breeding process. The knowledge of the relationship between cereal phenology and climatic data is important for understanding GEI and the physiological pathways responsible for the interaction effect. The grain yield of twenty winter barley genotypes in six environments was observed. Factors influencing the variability were analyzed using a linear mixed model. The partial least squares regression (PLSR) model was applied to determine the most relevant environmental variables in certain stages of development that explained GEI effects. Biplot with environmental variables explained 43.7% of the GEI. The barley was generally the most sensitive to the environmental conditions (relative humidity, maximum temperature and its variation, sun hours, and precipitation) during the anthesis and filling stage (May) which caused GEI. Temperature variables did not show significance only in the vegetative phase. Different genotypes responded differently to environmental factors. Genotypes NS-525, NS-589, and J-103 were highlighted as widely adaptable, and Zaječar was a suitable and reliable location for yield testing. The GEI information presented in this paper can be useful in traditional plant breeding and future breeding programs through molecular research of crop developmental genes and examination of physiological processes in two-row barley. [ABSTRACT FROM AUTHOR]

Estimating the fruit size is an important factor because it directly influences size-specific yield estimation, which would be useful for pricing in the market. In this paper, it was considered a method of constructing models for estimating the proportion of fruit size grades of citrus using polynomial regression. In order to construct models, curvilinear regressions were performed, utilizing the fruit diameters of a kind of citrus (Citrus junos Sieb. ex Tanaka) in the harvest. The constructed models were validated by comparison with another model, which was constructed using a combination of four datasets obtained from three orchards differing in the number of fruit sets. The estimation model's accuracy (EMA, defined as the sum of the absolute difference between the actual and estimated proportions of each grade) was used for the evaluation of constructed models. The EMAs of 14 models applied to 28 validation data were ranging from 2.0% to 6.1%. In all validations, the proportions of fruit size grade were insignificant at a 5% level by Pearson's chi-square test. Additionally, a comparison of EMAs differing in the number of trees by the constructed models showed that most were within EMA ≤ 10.0% in the case calculated by 10 trees. Validation of five farmers' orchards indicated that the EMA of two was within 10.0%, and the EMA of three was at 11.3 to 12.5%. These results revealed that the constructed models could be applied to orchards for differing numbers of fruit sets. The acceptable accuracy was derived by at least over 10 trees investigated at one time. [ABSTRACT FROM AUTHOR]

Aflatoxins, carcinogenic secondary metabolites produced by the Aspergillus fungi, pose a significant threat to groundnut, making them susceptible to infection and compromising their quality. Despite extensive breeding programs, the need for more durable resistance in groundnut germplasm remains a major challenge. Targeting susceptible genes favoring Aspergillus infection in groundnut could offer a promising strategy for achieving durable resistance. The glycine-rich RNA-binding protein (GR-RBP)-coding genes, known for their involvement in plant hypersensitivity and susceptibility to A. flavus, have been studied in model plants. However, there needs to be more understanding of the GR-RBP gene family in groundnut. In this study, twenty-three Arachis hypogaea GR-RBP (Ah.GR-RBP) genes were identified, and the chromosomal location, sub-cellular localization, and regulatory elements in the putative promoter region were analyzed. Expression analysis revealed that Ah.GR-RBP.1, Ah.GR-RBP.12, Ah.GR-RBP.3, and Ah.GR-RBP.15 showed higher expression in the susceptible genotype. This paper would help to provide knowledge on potential candidate target genes for precise breeding interventions for aflatoxin mitigation in groundnut. [ABSTRACT FROM AUTHOR]

The domestication of vines started in Asia 11,000 years ago, although it was not until the 19th century that oenology was established as a scientific discipline thanks to the research of Louis Pasteur on the role of microorganisms in wine fermentation. At the present time, the progression in next-generation sequencing (NGS) technologies is helping to facilitate the identification of microbial dynamics during winemaking. These advancements have aided winemakers in gaining a more comprehensive understanding of the role of microbiota in the fermentation process, which, in turn, is ultimately responsible for the delivery of provisioning (wine features and its production), regulating (such as carbon storage by vineyards, regulation of soil quality, and biocontrol of pests and diseases) or cultural (such as aesthetic values of vineyard landscapes, scholarly enjoyment of wine, and a sense of belonging in wine-growing regions) ecosystem services. To our knowledge, this is the first review of the state of knowledge on the role of microbiota in the delivery of ecosystem services in the wine sector, as well as the possibility of valuing them in monetary terms by operating logic chains, such as those suggested by the SEEA-EA framework. This paper concludes with a review of management practices that may enhance the value of microbiota ecosystem services and the role of smart farming in this task. [ABSTRACT FROM AUTHOR]

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model. [ABSTRACT FROM AUTHOR]

The leading role of Italy in the cultivation of durum wheat stimulated intense breeding activities in the country from the beginning of the 20th century, much earlier than in any other country involved in durum wheat production. Older, genetically more heterogeneous landraces were replaced with new, highly productive, superior quality varieties, and this led to an inevitable reduction in the overall genetic diversity among new cultivars, which makes the genetic variability preserved in old cultivars particularly valuable and important. The aim of this paper was to assist future breeding programs by providing a detailed description of the history of durum wheat breeding in Italy and of the changes in yield, quality, and related traits that subsequently occurred, starting from the most diffuse landraces present between 1900 and 1920 up until the present day. The parallel evolution of breeding techniques, breeding goals, and agricultural systems in this period is also described, and some future breeding goals suggested. In the current context of climate change and of rapidly mutating pathogen populations, preserving the yield level through the continuous introduction of new cultivars by exploiting the reservoir of largely unused genetic variation stored in old cultivars and landraces could be as important as increasing grain yield and quality. [ABSTRACT FROM AUTHOR]

In order to meet the needs of intensive mechanized picking in trellised pear orchards, this paper designed a pick-place integrated end-picker based on the analysis of agronomic characteristics of trellised pear gardens and fruit. In order to realize the accurate positioning of pears in picking, based on the kinematic analysis of robot arms and the construction of a private dataset, the YOLOv5s object detection algorithm was used in conjunction with a depth camera to achieve fruit positioning. The hand–eye system calibration was carried out. Aiming at solving the problems of redundancy, inefficiency, and uneven distribution of task volume in the conventional multiple robot arms algorithm, a simulated annealing algorithm was introduced to optimize the picking sequence, and a task allocation method was proposed. On the basis of studying several key parameters affecting the performance of the algorithm, the picking efficiency was greatly optimized. And the effectiveness of the proposed multi-robot collaborative picking method in a trellised pear orchard environment was demonstrated through experiments and simulation verification. The experiments showed that the picking efficiency of the integrated end-picker was increased by about 30%, and the success rate was significantly higher than that of the flexible grippers. The results of this study can be utilized to advance robotic pear-picking research and development. [ABSTRACT FROM AUTHOR]

The main purpose of the paper is to highlight the impact of foliar fertilization during the various growth stages of winter wheat and its role in achieving high-quality and superior production. Foliar fertilizers play a crucial role in calibrating and forming active growth intervals correlated with productivity factors. The research was conducted over a two-year period using the Andrada winter wheat variety. It involved the application of four different foliar fertilizers: basic mineral fertilization, FoliMAX Orange (two treatments and three treatments), FoliMAX CerealsMIX (two treatments and three treatments), and Microfert U (three treatments). Depending on the treatment, two or three applications of foliar with foliar fertilizers were carried out at various stages of winter wheat development, including pre-flowering and grain formation. The research method used to record physiological parameters was non-destructive (the leaves were not detached from the plant) and was based on the use of the CIRAS-3 foliar gas analyzer, which simultaneously determines several physiological and environmental indicators. As an indicator read by the device, photosynthesis was chosen for leaf temperature and leaf transpiration, correlated with production and quality indices. By applying the foliar fertilizer treatments to the wheat culture, we managed to increase production and improve the quality. After using different foliar fertilizers, the assimilation and physiological parameters had higher values of over 30 μmolm−2·s−1. Foliar fertilization applied to the winter wheat variety resulted in an average production increase between 450 and 765 kg·ha−1 and quality indices with a protein percentage between 11.5 and 12.6%, gluten content between 21.5 to 24.0% and the Zeleny index between 29.0 and 39.0%, and the mass of one thousand grains was between 48.0 and 50.5 g. [ABSTRACT FROM AUTHOR]

The use of nanoparticles, such as titanium dioxide (TiO2-NPs), has increased substantially over the years. Some of them will end up in the soil, where their effect on plants and the soil bacterial community needs to be studied to determine their possible environmental risks. In this paper, beans (Phaseolus vulgaris L.) were cultivated in soil with 0, 150, or 300 mg TiO2-NPs kg−1. Plant development, nodule formation, chlorophyl content, and the bacterial community were monitored in uncultivated, non-rhizosphere, and rhizosphere soils. TiO2-NPs did not affect the beans' growth and their chlorophyl content, but they did increase bacterial diversity and had a significant effect on the bacterial community structure in the rhizosphere, but not in the bulk and non-rhizosphere soil. Although the relative abundance of most bacterial groups varied with the TiO2-NP application rate, the cultivation of the bean plants, or the exposure time, that of Acidobacteria decreased, while that of Planctomycetes increased in the TiO2-NP-amended soil. Many bacterial groups were affected by the cultivation of the bean plants, i.e., the relative abundance of Acidobacteria, Gemmatimonadetes, Deltaproteobacteria, and Firmicutes mostly decreased in the rhizosphere independent of the application of TiO2-NPs or the time of exposure, while most groups belonging to Actinobacteria, Bacteroidetes, Rhizobiaceae, Phyllobacteriaceae, and Sphingomonadaceae were enriched. [ABSTRACT FROM AUTHOR]

Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose. [ABSTRACT FROM AUTHOR]

Inappropriate fertilisation results in the pollution of groundwater with nitrates and phosphates, eutrophication in surface water, emission of greenhouse gasses, and unwanted N deposition in natural environments, thereby harming the whole ecosystem. In greenhouses, the cultivation in closed-loop soilless culture systems (CLSs) allows for the collection and recycling of the drainage solution, thus minimising contamination of water resources by nutrient emissions originating from the fertigation effluents. Recycling of the DS represents an ecologically sound technology as it can reduce water consumption by 20–35% and fertiliser use by 40–50% in greenhouse crops, while minimising or even eliminating losses of nutrients, thereby preventing environmental pollution by NO3− and P. The nutrient supply in CLSs is largely based on the anticipated ratio between the mass of a nutrient absorbed by the crop and the volume of water, expressed as mmol L−1, commonly referenced to as "uptake concentration" (UC). However, although the UCs exhibit stability over time under optimal climatic conditions, some deviations at different locations and different cropping stages can occur, leading to the accumulation or depletion of nutrients in the root zone. Although these may be small in the short term, they can reach harmful levels when summed up over longer periods, resulting in serious nutrient imbalances and crop damage. To prevent large nutrient imbalances in the root zone, the composition of the supplied nutrient solution must be frequently readjusted, taking into consideration the current nutrient status in the root zone of the crop. The standard practice to estimate the current nutrient status in the root zone is to regularly collect samples of drainage solution and determine the nutrient concentrations through chemical analyses. However, as results from a chemical laboratory are available several days after sample selection, there is currently intensive research activity aiming to develop ion-selective electrodes (ISEs) for online measurement of the DS composition in real-time. Furthermore, innovative decision support systems (DSSs) fed with the analytical results transmitted either offline or online can substantially contribute to timely and appropriate readjustments of the nutrient supply using as feedback information the current nutrient status in the root zone. The purpose of the present paper is to review the currently applied technologies for nutrient and water recycling in CLSs, as well as the new trends based on ISEs and novel DSSs. Furthermore, a specialised DSS named NUTRISENSE, which can contribute to more efficient management of nutrient supply and salt accumulation in closed-loop soilless cultivations, is presented. [ABSTRACT FROM AUTHOR]

Strawberry (Fragaria × ananassa Duch.) has been widely accepted as the "Queen of Fruits". It has been identified as having high levels of vitamin C and antioxidants that are beneficial for maintaining cardiovascular health and maintaining blood sugar levels. The implementation of advanced techniques like precision agriculture (PA) is crucial for enhancing production compared to conventional farming methods. In recent years, the successful application of deep learning models was represented by convolutional neural networks (CNNs) in a variety of disciplines of computer vision (CV). Due to the dearth of a comprehensive and detailed discussion on the application of deep learning to strawberry cultivation, a particular review of recent technologies is needed. This paper provides an overview of recent advancements in strawberry cultivation utilizing Deep Learning (DL) techniques. It provides a comprehensive understanding of the most up-to-date techniques and methodologies used in this field by examining recent research. It also discusses the recent advanced variants of the DL model, along with a fundamental overview of CNN architecture. In addition, techniques for fine-tuning DL models have been covered. Besides, various strawberry-planting-related datasets were examined in the literature, and the limitations of using research models for real-time research have been discussed. [ABSTRACT FROM AUTHOR]

Global agricultural production is facing unprecedented challenges as the environment becomes increasingly polluted. Governments, scientists, companies and farmers are beginning to focus on appropriate environmental remediation and sustainable agricultural technologies and practices. Innovative environmental adaptation/remediation technologies have been developed and validated, including physical/chemical remediation, green sorbents and bioremediation. The development of environmental remediation technologies has provided additional tools and methods for global agri-environment and food security. The aim of this Special Issue is to bring together 21 cutting-edge research papers covering the latest developments in soil conditions, inorganic pollution, organic pollution, remediation technologies and monitoring methods. The four themes of the Special Issue are "Improvement of agricultural soil properties", "Remediation of potentially toxic element pollution", "Remediation of organic pollution" and "Ecosystem and crop assessment". Based on the results of this Special Issue, we find that combining the latest environmental pollution problems with advanced remediation technologies, continuously promoting technological innovation and policy support, and developing integrated new technologies for environmental protection will be future areas of research for sustainable agro-environmental development. [ABSTRACT FROM AUTHOR]

Currently, the simulation parameters for the model of the interaction between the transplanter, the plug seedlings, the soil, and the pot damage mechanism still need to be clarified. The optimization design of the planters and the improvement of planting quality are still urgent issues that need to be solved. In this paper, the simulation parameters of the pot and the soil were calibrated based on the pressure distribution measurement technology. With the actual collision impact force and matrix loss rate as the targets, a four-factor, three-level orthogonal test was designed to obtain the optimal parameters. Through the optimization analysis of the experimental results, it could be concluded that the pot–soil restitution coefficient, the pot–soil static friction coefficient, the pot–soil rolling friction coefficient, and the surface energy were 0.31, 0.88, 0.35, and 1.07 J/m2, respectively. The experimental verification of the optimal parameter combination showed that the relative error of the collision impact force was 1.65% and that the relative error of the matrix loss rate was 2.32%, which verified the model's reliability. Based on the optimal parameters, the movement law of the hole tray seedlings was studied at different positions during the transplanting process. The plug seedlings collided not only with the planter but also with the soil, which led to the breakage and looseness of the pot structure. The relative error between the matrix loss rate of the transplanter inserting soil, the matrix loss rate of the transplanter that did not enter the soil, and the simulated matrix loss rate was less than 10%, which further proved the accuracy of the simulation model. [ABSTRACT FROM AUTHOR]