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BACKGROUND: Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop‐specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets. RESULTS: Our results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61–98.76%), Mixed (90.76–92.31%), and Field (68.56–73.81%). Although CSMs were found to reduce inter‐crop disease misclassifications, they also led to a slight increase in intra‐crop misclassifications. CONCLUSION: Our findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field‐specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Straw returning has been shown to improve farmland soil, increase crop yield, and reduce global warming. This study investigated the models of six rice straw-returning techniques based on the life cycle assessment method. Compared to the direct modes, the indirect ones showed a greater environmental impact; up to 20.56 times in acidification and emission potentials. There was no significant difference in climate change among the six types. Except for the burning effect, all other modes showed improvement in soil fertility; phosphorus and organic matter content increased by 66.66% and 30.85%, respectively, microbial biomass carbon content doubled (105%), the four organic carbon components increased by >50%, crop morbidity was reduced, and diversity of soil fungus was improved. Feeding, as an indirect method for enhancing soil fertility and economic benefits, is set to emerge as a leading practice in China's straw management. However, straw-returning technology is limited by cost, greenhouse gases, and increased risk of diseases and pests. There is an urgent need for further improvement and development of carbon sequestration and emission reduction in China's agriculture. [ABSTRACT FROM AUTHOR]

Plant diseases, mainly caused by bacteria and fungi, affect crop yield and quality. Detecting disease symptoms at an early stage and promptly is a significant obstacle in safeguarding crops. In developing nations, experts and agronomists commonly opt for visual identification of diseases on vast farms, which incurs both time and monetary expenses. Scientists have suggested diverse deep neural network architectures for recognizing plant ailments. Nevertheless, deep learning algorithms necessitate a vast amount of parameters, which extends the training duration but yields commendable precision. While deep learning and Densenet are widely used in pesticide recommendations. Researchers have suggested diverse deep-learning architectures for detecting agricultural ailments and recommend appropriate pesticides. Test images were diagnosed using an automated Densenet model and the results were verified by plant pathologists. An accuracy of over 92% was achieved in identifying the disease. Our solution is an innovative, scalable and accessible tool for disease management of various crops that can be implemented as a cloud service for farmers and professionals involved sustainable agricultural production.

Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management.

Globally, phytopathogenic fungi infections cause crop diseases, resulting in crop yield and quality loss. Extensive use of chemical fungicides leads to resistance and high costs for growers as well as environmental pollution; thus, researchers are exploring a more sustainable approach using biological control tactics. This review highlights the critical processes involved in biological control by bacteria, fungi, viruses, and archaea, i.e., the synthesis of various metabolites, enzymes, and signaling molecules, as well as competitive tactics or soil suppressiveness that can effectively control the phytopathogenic fungi. The global increase in registrations for biological products reflects the rising demand and requirement for more organic agriculture and achieving some sustainable development targets. Understanding the complicated interplay between microorganisms in this environment can aid in managing soil diversity and inhibiting phytopathogenic fungi without chemical residues. Therefore, microorganisms are recommended as a sustainable alternative biological control. [ABSTRACT FROM AUTHOR]

Phytopathogenic fungi associated with roots and leaves can cause significant losses in crops of commercial interest due to alterations in the growth and development of the host plants. In addition, they could contaminate fruits in the postharvest stage, provoking significant economic damage. In this regard, biocontrol by antagonistic fungi such as Trichoderma sp. (Peerson, 1974) has been shown as a viable eco-friendly solution. Accordingly, in this study, four genera of native phytopathogenic fungi, namely Fusarium (Link, 1809), Botrytis (Micheli & Peersoon, 1729), Alternaria (Nees, 1817), and Colletotrichum (Corda, 1831)], as well as of native postharvest fungi, namely Rhizopus (Anton de Bary, 1886), Mucor (Saccardo, 1887), Penicillium (Friedrich, 1809), and Aspergillus (Micheli, 1728), were isolated and identified from the rhizosphere of multiple crops of an unstudied autochthonous region in Puebla, Mexico. The isolated phytopathogens were tested in dual confrontation assays against a native Trichoderma strain with presumable antagonistic activity, finding a significant growth inhibition, reported for the first time. For the phytopathogenic fungi, the highest percentage of inhibition of radial growth (PIRG) was observed in Fusarium sp., followed by Alternaria sp., and Colletotrichum sp.; for the post-harvesting fungi, the best PIRG was found in Penicillium sp. (2), followed by Aspergillus sp., Rhizopus sp., Mucor sp., and Penicillium sp. (1). [ABSTRACT FROM AUTHOR]

Plant diseases, mainly caused by bacteria and fungi, affect crop yield and quality. Detecting disease symptoms at an early stage and promptly is a significant obstacle in safeguarding crops. In developing nations, experts and agronomists commonly opt for visual identification of diseases on vast farms, which incurs both time and monetary expenses. Scientists have suggested diverse deep neural network architectures for recognizing plant ailments. Nevertheless, deep learning algorithms necessitate a vast amount of parameters, which extends the training duration but yields commendable precision. While deep learning and Densenet are widely used in pesticide recommendations. Researchers have suggested diverse deep-learning architectures for detecting agricultural ailments and recommend appropriate pesticides. Test images were diagnosed using an automated Densenet model and the results were verified by plant pathologists. An accuracy of over 92% was achieved in identifying the disease. Our solution is an innovative, scalable and accessible tool for disease management of various crops that can be implemented as a cloud service for farmers and professionals involved sustainable agricultural production. [ABSTRACT FROM AUTHOR]

Soils are termed 'suppressive' when they can limit the emergence and propagation of plant diseases. However, little is known regarding what factors determine suppressiveness and whether they could be improved for a given soil. Agricultural practices, such as tillage systems, influence the properties of the soil, and could be a lever to improve soil suppression of pathogens. In this study, we investigated the impact of soil type and crop management practices on soil suppressiveness towards Fusarium graminearum, a major winter wheat (Triticum aestivum) pathogen. As it is transmitted via crop residues left on soil, F. graminearum is susceptible to soil suppression. Soil suppressiveness has been evaluated in more than a 100 sampled fields in the Limagne plain (Puy-de-Dôme, France), to represent a great diversity of soil types (either calcisol, vertisol or fluvisol) and cropping systems (organic, reduced tillage or intensive cropping systems). The physicochemical composition of the sampled soils and crop management practices, identified through farmer surveys, were included in a generalized linear mixed-effect model to explain soil fungistasis. A fungistasis test was performed by putting a plug of PDA-agar inoculated with Fusarium in contact with the soil and measuring the area of fungal growth. This test revealed a wide variety of soil fungistatic properties, ranging from conducive soils (22% of soil covered by fungal growth) to very suppressive soils (1% of soil covered). Suppressiveness was related to soil perennial properties (p-value <.001) rather than practices. Overall, these results show that soil type and composition play a key role in fungistasis, and that soil suppressiveness is a complex, multifactorial process. [ABSTRACT FROM AUTHOR]

Phytopathogenic fungi associated with roots and leaves can cause significant losses in crops of commercial interest due to alterations in the growth and development of the host plants. In addition, they could contaminate fruits in the postharvest stage, provoking significant economic damage. In this regard, biocontrol by antagonistic fungi such as Trichoderma sp. (Peerson, 1974) has been shown as a viable eco-friendly solution. Accordingly, in this study, four genera of native phytopathogenic fungi, namely Fusarium (Link, 1809), Botrytis (Micheli & Peersoon, 1729), Alternaria (Nees, 1817), and Colletotrichum (Corda, 1831)], as well as of native postharvest fungi, namely Rhizopus (Anton de Bary, 1886), Mucor (Saccardo, 1887), Penicillium (Friedrich, 1809), and Aspergillus (Micheli, 1728), were isolated and identified from the rhizosphere of multiple crops of an unstudied autochthonous region in Puebla, Mexico. The isolated phytopathogens were tested in dual confrontation assays against a native Trichoderma strain with presumable antagonistic activity, finding a significant growth inhibition, reported for the first time. For the phytopathogenic fungi, the highest percentage of inhibition of radial growth (PIRG) was observed in Fusarium sp., followed by Alternaria sp., and Colletotrichum sp.; for the post-harvesting fungi, the best PIRG was found in Penicillium sp. (2), followed by Aspergillus sp., Rhizopus sp., Mucor sp., and Penicillium sp. (1).

Straw returning has been shown to improve farmland soil, increase crop yield, and reduce global warming. This study investigated the models of six rice straw-returning techniques based on the life cycle assessment method. Compared to the direct modes, the indirect ones showed a greater environmental impact; up to 20.56 times in acidification and emission potentials. There was no significant difference in climate change among the six types. Except for the burning effect, all other modes showed improvement in soil fertility; phosphorus and organic matter content increased by 66.66% and 30.85%, respectively, microbial biomass carbon content doubled (105%), the four organic carbon components increased by >50%, crop morbidity was reduced, and diversity of soil fungus was improved. Feeding, as an indirect method for enhancing soil fertility and economic benefits, is set to emerge as a leading practice in China’s straw management. However, straw-returning technology is limited by cost, greenhouse gases, and increased risk of diseases and pests. There is an urgent need for further improvement and development of carbon sequestration and emission reduction in China’s agriculture.

Timely detection of pests and diseases in crops is essential to mitigate severe damage and economic losses, especially in the context of climate change. This paper describes a method for detecting the presence of coffee leaf rust (CLR) using two databases: RoCoLe and a database obtained from an unmanned aerial vehicle (UAV) equipped with an RGB camera. The developed method follows a two-stage approach. In the first stage, images are processed using ImageJ software, while, in the second phase, Python is used to implement morphological filters and the Hough transform for rust identification. The algorithm’s performance is evaluated using the chi-square test, and its discriminatory capacity is assessed through the generation of a Receiver Operating Characteristic (ROC) curve. Additionally, Cohen’s kappa method is used to assess the agreement among observers, while Kendall’s rank correlation coefficient (KRCC) measures the correlation between the criteria of the observers and the classifications generated by the method. The results demonstrate that the developed method achieved an efficiency of 97% in detecting coffee rust in the RoCoLe dataset and over 93.5% in UAV images. These findings suggest that the developed method has the potential to be implemented in the future on a UAV for rust detection.

Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases. The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes. Through rigorous training and evaluation, the proposed system achieved an impressive accuracy rate of 99%. This mobile application serves as a convenient and valuable advisory tool, providing early detection and guidance in real agricultural environments. The significance of this research lies in its potential to revolutionize plant disease detection and management practices. By automating the identification process through deep learning algorithms, the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise. The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops. [ABSTRACT FROM AUTHOR]

Mentioned for the first time in an article 1971, the occurrence of the term "Macrophomina phaseolina" has experienced a steep increase in the scientific literature over the past 15 years. Concurrently, incidences of M. phaseolinacaused crop diseases have been getting more frequent. The high levels of diversity and plasticity observed for M. phasolina genomes along with a rich equipment of plant cell wall degrading enzymes, secondary metabolites and putative virulence effectors as well as the unusual longevity of microsclerotia, their asexual reproduction structures, make this pathogen very difficult to control and crop protection against it very challenging. During the past years several studies have emerged reporting on host defense measures against M. phaseolina, as well as mechanisms of pathogenicity employed by this fungal pathogen. While most of these studies have been performed in crop systems, such as soybean or sesame, recently interactions of M. phaseolina with the model plant Arabidopsis thaliana have been described. Collectively, results from various studies are hinting at a complex infection cycle of M. phaseolina, which exhibits an early biotrophic phase and switches to necrotrophy at later time points during the infection process. Consequently, responses of the hosts are complex and seem coordinated by mul t iple defense-associated phytohormones. However, at this point no robust and strong host defense mechanism against M. phaseolina has been described. [ABSTRACT FROM AUTHOR]

Timely detection of pests and diseases in crops is essential to mitigate severe damage and economic losses, especially in the context of climate change. This paper describes a method for detecting the presence of coffee leaf rust (CLR) using two databases: RoCoLe and a database obtained from an unmanned aerial vehicle (UAV) equipped with an RGB camera. The developed method follows a two-stage approach. In the first stage, images are processed using ImageJ software, while, in the second phase, Python is used to implement morphological filters and the Hough transform for rust identification. The algorithm's performance is evaluated using the chi-square test, and its discriminatory capacity is assessed through the generation of a Receiver Operating Characteristic (ROC) curve. Additionally, Cohen's kappa method is used to assess the agreement among observers, while Kendall's rank correlation coefficient (KRCC) measures the correlation between the criteria of the observers and the classifications generated by the method. The results demonstrate that the developed method achieved an efficiency of 97% in detecting coffee rust in the RoCoLe dataset and over 93.5% in UAV images. These findings suggest that the developed method has the potential to be implemented in the future on a UAV for rust detection. [ABSTRACT FROM AUTHOR]

[Display omitted] Preventing plant loss and improving their health status are essential for agricultural industry. Correspondingly, the deprivation of plants severely impacts our ecological system. As such, global efforts have been intensely made to promote the development of advanced sensing and treatment platforms to forestall plant loss. Existing technologies mainly encounter a number of challenges in providing results in a non-invasive, rapid turnaround, and affordable fashions. Accordingly, notable progressions in innovative approaches—particularly biosensing and delivery platforms, are vastly required for agriculture realm. In this regard, microneedles have emerged as a pivotal technological tool that plays multifaceted roles in biosensing and delivery systems, with attention of growing towards agriculture. Simply put, microneedles offer several advantages over conventional methods for being less invasive, rapid, and highly precise. In this review, recent advancements in microneedle technologies including their implementations in agriculture are highlighted coherently. In particular, extracting DNA from plant leaves and expressing transient genes using microneedles are elaborated in details. Microneedle-based sensing platforms for detecting essential compounds and secondary metabolites are discussed as well. Recent advances focusing the delivery of agrochemicals and nanotherapeutics via microneedles are elaborated. By this means, this review aims to bridge the existing gaps between microneedles and agriculture precisely. [ABSTRACT FROM AUTHOR]

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Mentioned for the first time in an article 1971, the occurrence of the term “Macrophomina phaseolina” has experienced a steep increase in the scientific literature over the past 15 years. Concurrently, incidences of M. phaseolina-caused crop diseases have been getting more frequent. The high levels of diversity and plasticity observed for M. phasolina genomes along with a rich equipment of plant cell wall degrading enzymes, secondary metabolites and putative virulence effectors as well as the unusual longevity of microsclerotia, their asexual reproduction structures, make this pathogen very difficult to control and crop protection against it very challenging. During the past years several studies have emerged reporting on host defense measures against M. phaseolina, as well as mechanisms of pathogenicity employed by this fungal pathogen. While most of these studies have been performed in crop systems, such as soybean or sesame, recently interactions of M. phaseolina with the model plant Arabidopsis thaliana have been described. Collectively, results from various studies are hinting at a complex infection cycle of M. phaseolina, which exhibits an early biotrophic phase and switches to necrotrophy at later time points during the infection process. Consequently, responses of the hosts are complex and seem coordinated by multiple defense-associated phytohormones. However, at this point no robust and strong host defense mechanism against M. phaseolina has been described.

Precisely discerning disease types and vulnerable areas is crucial in implementing effective monitoring of crop production. This forms the basis for generating targeted plant protection recommendations and automatic, precise applications. In this study, we constructed a dataset comprising six types of field maize leaf images and developed a framework for classifying and localizing maize leaf diseases. Our approach involved integrating lightweight convolutional neural networks with interpretable AI algorithms, which resulted in high classification accuracy and fast detection speeds. To evaluate the performance of our framework, we tested the mean Intersection over Union (mIoU) of localized disease spot coverage and actual disease spot coverage when relying solely on image-level annotations. The results showed that our framework achieved a mIoU of up to 55.302%, indicating the feasibility of using weakly supervised semantic segmentation based on class activation mapping techniques for identifying disease spots in crop disease detection. This approach, which combines deep learning models with visualization techniques, improves the interpretability of the deep learning models and achieves successful localization of infected areas of maize leaves through weakly supervised learning. The framework allows for smart monitoring of crop diseases and plant protection operations usingmobile phones, smart farm machines, and other devices. Furthermore, it offers a reference for deep learning research on crop diseases. [ABSTRACT FROM AUTHOR]

As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed. [ABSTRACT FROM AUTHOR]

Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%–23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases' detection. [ABSTRACT FROM AUTHOR]

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model's effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production. [ABSTRACT FROM AUTHOR]

Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the apple disease prediction system concerning different types of disease found in the apple crop are discussed. In addition, this paper presents the contributions made by numerous researchers over the past few years in the apple disease prediction system. The aim of this research is to support the development of smart agriculture applications that would be helpful in precision farming for the optimization of resources with the help of IoT in agriculture and early disease prediction in apple crops. [ABSTRACT FROM AUTHOR]

Annually, significant crop losses are reported due to diseases caused by phytopathogens. Most subsistence farmers cannot afford the high cost of chemical treatments thereby resulting in the increasing dependence on plant extracts to manage crop diseases. In this study, we documented plants used for the management of cabbage and spinach diseases in OR Tambo Municipality, Eastern Cape Province. An ethnobotanical survey using semi-structured questionnaires was used to document plants and plant parts used by the subsistence farmers in managing cabbage and spinach diseases. Semi-structured questionnaires were administered to 41 consenting subsistence farmers from November to December in 2021, using snowball sampling. The collected data were subjected to descriptive statistical and ethnobotanical analyses. A total of 17 plants belonging to 10 families were identified by the participants as being used in mitigating cabbage and spinach diseases. Tulbaghia violacea, Aloe ferox, and Capsicum annuum had the highest use value of 0.32 each, whereas Tulbaghia violacea had the highest relative frequency of citation of 0.39. This current study revealed the importance of plants in managing crop diseases in local communities. It provides baseline data for future pharmacological evaluations in authenticating the efficacies of the identified plants in managing crop diseases. [ABSTRACT FROM AUTHOR]

Crop disease recognition plays a crucial role in agricultural production. However, disease images are large in scale and have a lot of redundant information, which reduces the effectiveness of deep neural networks in extracting diseases. To address the above issues and considering that not all image regions are relevant to disease recognition, this study proposes an efficient crop disease recognition method with dynamic reduction of image redundancy. The method is a two-stage process. In the first stage, we employ the lightweight CA-AnchorNet, which incorporates coordinate attention, to swiftly generate a feature map of the affected crop areas. Subsequently, class activation maps (CAMs) are utilized to identify the disease feature regions, highlighting areas that exhibit class discriminability. These regions are then mapped to a higher resolution from the original lower-resolution image, and the target patch is extracted. In the second stage, these local semantic patches, characterized by reduced spatial redundancy, are fed into the lightweight PatchNet for accurate recognition. PatchNet incorporates the Inception-C module and the ACON-C activation function. These features enhance the model's ability to express multi-scale and non-linear characteristics of crop disease features. This method does not require manually annotated region boxes and achieves an identification accuracy of 99.86 % on a 12-class crop disease dataset with complex environments. The parameter count is only 0.98 M. This method has the characteristics of accurate localization and low parameter count, and can be used for effective and high-precision recognition of crop diseases in complex environments. • Coordinate attention mechanism was used to design a lightweight CA-AnchorNet. • A lightweight CA-AnchorNet and PatchNet constitute a two-stage framework. • A patch localization algorithm was designed through a class activation map. • A multi-scale lightweight PatchNet network is proposed. [ABSTRACT FROM AUTHOR]

Crop diseases are increasingly causing devastating yield losses each year, posing a significant threat to global food security. Currently, fungicide treatment is one of the most effective measures to mitigate these disease-induced yield losses. Accurately predicting disease occurrence, onset timing, and development is critical for optimizing fungicide application schedules to achieve high efficacy. In this study, we compiled weekly disease observations, crop management, and weather data from 207 locations across China, India, and Japan. We compared six machine learning (ML) models for their performance in simulating disease severity. By mimicking disease development curves and trends at early stages, we developed a novel change detection (CD) model, named rolling linear regression (RLR), and combined it with the best-performing ML model to predict the occurrence, severity, and onset dates of four major rice diseases: leaf blast (Magnaporthe oryzae), panicle-neck blast (Magnaporthe oryzae), sheath blight (Rhizoctonia solani), and false smut (Ustilaginoidea virens). Our findings showed that the RandomForestRegressor demonstrated the highest performance in simulating disease severity, with mean absolute errors (MAE) below 0.5 % and root mean square errors (RMSE) below 2.5 %. The RLR model showed a distinct advantage over the other four widely used CD models based on five evaluation metrics. Additionally, the paired RandomForestRegressor+RLR model achieved the highest F1-score, ranging from 0.7 to 0.8, in predicting disease occurrence, outperforming 29 other ML+CD model pairs. Furthermore, the RandomForestRegressor+RLR model predicted onset dates with fewer than 6 error days and accuracies ranging from 74 % to 87 %. Combining ML with CD models not only shows robust generalization across diverse environmental conditions but also proves highly effective for large-scale disease risk forecasting in rice farming regions. The adaptability of ML techniques, when sufficient training data are available, particularly enhances decision support systems aimed at optimizing rice disease management practices for growers across various regions. Our hybrid model thus presents a compelling advancement in the precision agriculture domain, with significant implications for improving disease management strategies for crops beyond rice through timely intervention. This approach can contribute to safeguarding global food security by reducing crop losses due to disease damage. [Display omitted] • Compared 6 machine learning models to simulate severity of four rice diseases. • Developed a change detection model for occurrence and onset detection. • Achieved disease severity simulation with MAE < 0.5 % and RMSE < 2.5 %. • Hybrid model predicted onset dates with MAE<6 days for more 70 % of trials. • Utilized model to aid farmers' decision-making in rice disease management. [ABSTRACT FROM AUTHOR]

Datasets are very important in image recognition research based on machine learning methods. In particular, advanced methods such as deep learning and transfer learning are more dependent on datasets used for training models. The quality of datasets directly affects the final effect of these methods. In the research of crop disease image recognition, due to the complication of the agricultural environment and the variety of crops, datasets are scarce at present. Therefore, more and more researches adopt methods based on transfer learning, which can make up for the lack of data in the target domain with the help of other datasets. Among these methods, the selection of auxiliary domain datasets has great impact on the modeling effect of target domain. In order to clarify the impact of datasets on the research of crop disease image recognition, this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer learning. The selected datasets include PlantVillage and Image Database for Agricultural Diseases and Pests Research (IDADP), which have been widely used in recent studies. And the selected deep neural network frameworks include ResNet50, InceptionV3, and EfficientNet. In the method of this study, the datasets are preprocessed first, such as data enhancement. After dividing the auxiliary domain and the target domain, the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset. Finally, the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target. In the experiments, multiple different datasets and different models were tested and compared. The results show that when the test set samples and training sample scenarios are consistent, the recognition accuracy of different network frameworks on multiple test sets is generally high. When the scenarios of test set samples and training samples are inconsistent, the recognition of various test sets by different network models cannot obtain ideal results. For the recognition of crop disease images that are collected from the actual cultivation environment, the use of IDADP dataset modeling is better, and it has more practical value in the actual application of crop disease image recognition. [ABSTRACT FROM AUTHOR]

To achieve sustainable disease management in agriculture, there's a growing interest in using beneficial microorganisms as alternatives to chemical pesticides. Bacteria, in particular, have been extensively studied as biological control agents, but their inconsistent performance and limited availability hinder broader adoption. Research continues to explore innovative biocontrol technologies, which can be enhanced by combining silicon (Si) with biocontrol plant growth-promoting rhizobacteria (PGPR). Both biocontrol PGPR and Si demonstrate effectiveness in reducing plant disease under stress conditions, potentially leading to synergistic effects when used together. This review examines the individual mechanisms by which biocontrol PGPR and Si fertilizers manage plant diseases, emphasizing their roles in enhancing plant defense and decreasing disease incidence. Various Si fertilizer sources allow for flexible application methods, suitable for different target diseases and plant species. However, challenges exist, such as inconsistent soil Si data, lack of standardized soil tests, and limited availability of Si fertilizers. Addressing these issues necessitates collaborative efforts to develop improved Si fertilizers and tailored application strategies for specific cropping systems. Additionally, exploring silicate-solubilizing biocontrol bacteria to enhance Si availability in soils introduces intriguing research avenues. Investigating these bacteria's diversity and mechanisms can optimize Si access for plants and bolster disease resistance. Overall, combining biocontrol PGPR and Si fertilizers or silicate-solubilizing biocontrol bacteria shows promise for sustainable agriculture, enhancing crop productivity while reducing reliance on chemical inputs and promoting environmental sustainability., Competing Interests: Declaration of competing interest The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)