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Effective pathogen detection is essential for plant disease control. However, plant sample preparation for downstream assays, such as quantitative polymerase chain reaction (qPCR), is challenging to perform outside of a laboratory. This paper reports two sample preparation methods featuring chemical and mechanical lysis and nucleic acid extraction using a micro-homogenizer, followed by serial dilution or nucleic acid purification with a paper disk before assay. Five minutes of lysis and extraction resulted in DNA and RNA yields of up to 76.5% and 63.3%, respectively, compared to mortar and pestle controls. Crude lysates were unsuitable for direct use in qPCR assays; however, serial dilution or quick wash using chromatography paper rendered samples ready for such assays. Additionally, the nucleic acids stored on paper disks under various storage conditions remained stable for one month. These methods can facilitate the in-field preparation of citrus samples and allow for both onsite and mail-in diagnostics for growers. [ABSTRACT FROM AUTHOR]

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper's improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately. [ABSTRACT FROM AUTHOR]

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture. [ABSTRACT FROM AUTHOR]

This paper discusses the mechanisms by which fungi manipulate plant physiology and suppress plant defense responses by producing effectors that can target various host proteins. Effector-triggered immunity and effector-triggered susceptibility are pivotal elements in the complex molecular dialogue underlying plant–pathogen interactions. Pathogen-produced effector molecules possess the ability to mimic pathogen-associated molecular patterns or hinder the binding of pattern recognition receptors. Effectors can directly target nucleotide-binding domain, leucine-rich repeat receptors, or manipulate downstream signaling components to suppress plant defense. Interactions between these effectors and receptor-like kinases in host plants are critical in this process. Biotrophic fungi adeptly exploit the signaling networks of key plant hormones, including salicylic acid, jasmonic acid, abscisic acid, and ethylene, to establish a compatible interaction with their plant hosts. Overall, the paper highlights the importance of understanding the complex interplay between plant defense mechanisms and fungal effectors to develop effective strategies for plant disease management. [ABSTRACT FROM AUTHOR]

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model's receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value. [ABSTRACT FROM AUTHOR]

The immune response in plants is regulated by several phytohormones and involves the overexpression of defense genes, including the pathogenesis-related ( PR- ) genes. The data reported in this paper indicate that nematodes can suppress the immune response by inhibiting the expression of defense genes. Transcripts from nine defense genes were detected by qRT-PCR in the roots of tomato plants at three and seven days post-inoculation (dpi) with living juveniles (J2s) of Meloidogyne incognita (root-knot nematodes, RKNs). All the salicylic acid (SA)-responsive genes tested ( PR-1 , PR-2 , PR-4b , PR-5 ) were down-regulated in response to nematode infection. On the contrary, the expression of jasmonic acid (JA)-responsive genes, including ACO (encoding the enzyme 1-aminocyclopropane-1-carboxylic acid oxidase, which catalyzes the last step of ethylene (ET) biosynthesis) and JERF3 ( Jasmonate Ethylene Response Factor 3 ), was unaffected by the infection. Conversely, the effect of nematode attack on the activities of the defense enzymes endoglucanase and endochitinase, encoded by PR-2 and PR-3, respectively, changed depending on the tested dpi. At 5 dpi, both enzymes were inhibited in inoculated plants compared to healthy controls. The genes encoding glutathione peroxidase ( GPX ) and catalase ( CAT ), both part of the antioxidant plant system, were highly overexpressed. Additionally, the activity of the antioxidant enzymes superoxide dismutase (SOD), CAT, and ascorbate peroxidase (APX) was enhanced in infected roots. Isoelectrofocusing of root extracts revealed novel SOD isoforms in samples from inoculated plants. Furthermore, plants were pre-treated with an array of key compounds, including hormone generators, inhibitors of SA or JA-mediated defense pathways, reactive oxygen species (ROS) scavengers and generators, inhibitors of ROS generation, and compounds that interfere with calcium-mediated metabolism. After treatments, plants were inoculated with RKNs, and nematodes were allowed to complete their life cycle. Factors of plant growth and infection level in treated plants were compared with those from untreated inoculated plants. Generally, compounds that decreased SA and/or ROS levels increased infection severity, while those that reduced JA/ET levels did not affect infection rates. ROS generators induced resistance against the pests. Compounds that silence calcium signaling by preventing its intake augmented infection symptoms. The data shown in this paper indicate that SA-mediated plant immune responses are consistently suppressed during the early stages of nematode infection, and this restriction is associated with the activation of the antioxidant ROS-scavenging system.

Fusarium sacchari is a significant pathogenic fungus that causes sugarcane Pokkah Boeng. Proteins secreted by pathogenic fungi can be delivered into hosts to suppress plant immunity and establish infection. However, there is still much to be discovered regarding F. sacchari 's secreted effectors in overcoming plant immunity. In this paper, we characterize a novel effector called FsMEP1, which is essential for the virulence of F. sacchari . FsMEP1 contains a conserved zinc-binding motif sequence, HEXXH, and is highly expressed during host infection. Using the Agrobacterium tumefaciens -mediated transient expression system, it was confirmed that FsMEP1 could suppress Bcl-2-associated X protein (BAX)-triggered cell death, callose deposition, and ROS explosion in Nicotiana benthamiana . Furthermore, the deletion of FsMEP1 demonstrated its requirement for contributing to the pathogenicity of F. sacchari in sugarcane. Further analysis revealed that FsMEP1 could interact with the sugarcane thiamine thiazole synthase ScTHI2 and disrupt its normal localization, thereby inhibiting the synthesis of thiamine and the defense responses mediated by ScTHI2. Based on these findings, we propose that ScTHI2 represents a potential molecular target for improving sugarcane resistance to Pokkah Boeng disease.

A transcription-aided selection (TAS) strategy is proposed in this paper, which utilizes the positive regulatory roles of genes involved in the plant immunity pathways to screen crops with high disease resistance. Increased evidence has demonstrated that upon pathogen attack, the expression of diverse genes involved in salicylic acid (SA)-mediated SAR are differentially expressed and transcriptionally regulated. The paper discusses the molecular mechanisms of the SA signaling pathway, which plays a central role in plant immunity, and identifies differentially expressed genes (DEGs) that could be targeted for transcriptional detection. We have conducted a series of experiments to test the TAS strategy and found that the level of GmSAGT1 expression is highly correlated with soybean downy mildew (SDM) resistance with a correlation coefficient R 2 = 0.7981. Using RT-PCR, we screened 2501 soybean germplasms and selected 26 collections with higher levels of both GmSAGT1 and GmPR1 (Pathogenesis-related proteins1) gene expression. Twenty-three out of the twenty-six lines were inoculated with Peronospora manshurica (Pm) in a greenhouse. Eight showed HR (highly resistant), four were R (resistant), five were MR (moderately resistant), three were S (susceptible), and three were HS (highly susceptible). The correlation coefficient R 2 between the TAS result and Pm inoculation results was 0.7035, indicating a satisfactory consistency. The authors anticipate that TAS provides an effective strategy for screening crops with broad-spectrum and long-lasting resistance.

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model's accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model's ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification.

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization. [ABSTRACT FROM AUTHOR]

In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. Experimental results further confirm that the proposed framework is effective with imbalanced and small data. The backbone network integrated with this framework is lightweight with approximately 2.9629 million trainable parameters, which is second to SqueezeNet and significantly lower than other lightweight deep networks. Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labeled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performance and struggle to handle imbalanced data. [ABSTRACT FROM AUTHOR]

Simple Summary: A range of fruits and vegetables including tomatoes, cucumbers, peppers, and lettuce are grown in hydroponic systems. These systems do not contain soil and instead use a circulating nutrient solution to grow the plants. Removing soil from the growing systems changes the microscopic organisms that are present at the root of the plants. Fungi and oomycetes, a fungal-like organism, are both present in these systems. Some of these organisms promote plant growth, while others cause disease and crop loss. This paper reviews current literature on seven organisms that cause disease in plants, as well as four beneficial organisms. We explain the strategies beneficial fungi use to help plants grow, including direct killing of pathogens, strengthening the plant's immune system, and enabling the plant to gain more nutrients from the environment. Additionally, we explain how phytopathogens spread throughout hydroponic systems and destroy plant tissue. The aim of this review is to provide readers with management strategies to reduce harmful plant diseases in hydroponic operations, as well as beneficial options to increase crop yields. Both scientists and growers can benefit from a complete understanding of the interactions between plants, fungi, and oomycetes. Hydroponic systems are examples of controlled environment agriculture (CEA) and present a promising alternative to traditional farming methods by increasing productivity, profitability, and sustainability. In hydroponic systems, crops are grown in the absence of soil and thus lack the native soil microbial community. This review focuses on fungi and oomycetes, both beneficial and pathogenic, that can colonize crops and persist in hydroponic systems. The symptomatology and mechanisms of pathogenesis for Botrytis, Colletotrichum, Fulvia, Fusarium, Phytophthora, Pythium, and Sclerotinia are explored for phytopathogenic fungi that target floral organs, leaves, roots, and vasculature of economically important hydroponic crops. Additionally, this review thoroughly explores the use of plant growth-promoting fungi (PGPF) to combat phytopathogens and increase hydroponic crop productivity; details of PGP strategies and mechanisms are discussed. The benefits of Aspergillus, Penicillium, Taloromyces, and Trichoderma to hydroponics systems are explored in detail. The culmination of these areas of research serves to improve the current understanding of the role of beneficial and pathogenic fungi, specifically in the hydroponic microbiome. [ABSTRACT FROM AUTHOR]

Crop diseases pose a significant threat to global food security, with both economic and environmental consequences. Early and accurate detection is essential for timely intervention and sustainable farming. This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. The review method includes an in-depth analysis of algorithm performance using key metrics such as accuracy, precision, recall, and F1 score across various datasets. We also highlight the data imbalances in commonly used datasets, particularly PlantVillage, and discuss the challenges posed by these imbalances. The research highlights critical insights regarding ML and DL models in crop disease detection. A primary challenge identified is the imbalance in the PlantVillage dataset, with a high number of healthy images and a strong bias toward certain disease categories like fungi, leaving other categories like mites and molds underrepresented. This imbalance complicates model generalization, indicating a need for preprocessing steps to enhance performance. This study also shows that combining Vision Transformers (ViTs) with Green Chromatic Coordinates and hybridizing these with SVM achieves high classification accuracy, emphasizing the value of advanced feature extraction techniques in improving model efficacy. In terms of comparative performance, DL architectures like ResNet50, VGG16, and convolutional neural network demonstrated robust accuracy (95–99%) across diverse datasets, underscoring their effectiveness in managing complex image data. Additionally, traditional ML models exhibited varied strengths; for instance, SVM performed better on balanced datasets, while RF excelled with imbalanced data. Preprocessing methods like K-means clustering, Fuzzy C-Means, and PCA, along with ensemble approaches, further improved model accuracy. Lastly, the study underscores that high-quality, well-labeled datasets, stakeholder involvement, and comprehensive evaluation metrics such as F1 score and precision are crucial for optimizing ML and DL models, making them more effective for real-world applications in sustainable agriculture. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases. To ensure the model's robustness and generalizability beyond the training dataset, it was further tested on a set of unseen images sourced from Google Images. This additional testing aimed to assess the model's effectiveness in real-world scenarios, where it might encounter new data. The evaluation results were auspicious, demonstrating the model's capability to classify plant conditions, such as diseases, with high accuracy. Moreover, the use of YOLOv8 offers significant improvements in speed and precision, making it suitable for real-time plant disease monitoring applications. The findings highlight the potential of this methodology for broader agricultural applications, including early disease detection and prevention. [ABSTRACT FROM AUTHOR]

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management. [ABSTRACT FROM AUTHOR]

Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is important to detect the robustness of DNNs through adversarial attacks. The paper firstly improves the EfficientNet by adding the SimAM attention module. The SimAM-EfficientNet is proposed in this paper. The experimental results show that the accuracy of the improved model on PlantVillage reaches 99.31%. The accuracy of ResNet50 is 98.33%. The accuracy of ResNet18 is 98.31%. The accuracy of DenseNet is 98.90%. In addition, the GP-MI-FGSM adversarial attack algorithm improved by gamma correction and image pyramid in this paper can increase the success rate of attack. The model proposed in this paper has an error rate of 87.6% whenattacked by the GP-MI-FGSM adversarial attack algorithm. The success rate of GP-MI-FGSM proposed in this paper is higher than other adversarial attack algorithms, including FGSM, I-FGSM, and MI-FGSM. [ABSTRACT FROM AUTHOR]

Essential oils (EOs) are concentrated hydrophobic liquids that originate from plants and contain different bioactive chemicals and volatile substances. Several plant essential oils (PEOs) are obtained from a variety of medicinal plants and have been utilized in folk medicine and traditional pharmacopoeia. They have a long history of usage as antibacterial medicines to treat various human, animal, and plant diseases. The extraction of essential oils frequently involves fractional distillation with a variety of organic solvents. EOs can be used successfully in the food and cosmetics industries in addition to their traditional use as antimicrobial agents. This Special Issue covers various significant PEOs and their individual chemical constituents and biological-pharmaceutical functions. Further information focused on the chemical characterizations, modes of action, and biopharmaceutical properties of PEOs. This Special Issue includes seventeen research papers from different geographical zones. [ABSTRACT FROM AUTHOR]

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection. [ABSTRACT FROM AUTHOR]

Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases' using unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Colletotrichum boninense is the main pathogenic fungus causing leaf spot disease in Sorghum sudangrass hybrids , which seriously impairs its quality and yield. In order to find an efficient and green means of control, this study used the agar disk diffusion method to screen for a fungicide with the strongest inhibitory effect on C. boninense from among several bacteria, fungi, and chemicals. Then, the changes in the plant's antioxidant system and metabolic levels after treatment were used to compare the three means of control. The lowest inhibitory concentration of Zalfexam was 10 mg/mL, at which point C. boninense did not grow, and the inhibition rates of Bacillus velezensis (X7) and Trichoderma harzianum were 33.87-51.85% and 77.86-80.56%, respectively. Superoxide dismutase (SOD) and chitinase were up-regulated 2.43 and 1.24 folds in the Trichoderma harzianum group (M group) and SOD activity was up-regulated 2.2 folds in the Bacillus velezensis group (X7 group) compared to the control group (CK group). SOD, peroxidase (POD), and chitinase activities were elevated in the Zalfexam group (HX group). The differential metabolites in different treatment groups were mainly enriched in amino acid metabolism and production, flavonoid production, and lipid metabolism pathways. Compared with the diseased plants (ZB group), the M, X7, HX, and CK groups were co-enriched in the tryptophan metabolic pathway and glutamate-arginine metabolic pathway, and only the CK group showed a down-regulation of the metabolites in the two common pathways, while the metabolites of the common pathways were up-regulated in the M, X7, and HX groups. In addition, the salicylic acid-jasmonic acid pathway and ascorbic acid-glutathione, which were unique to the M group, played an important role in helping Sorghum sudangrass hybrids to acquire systemic resistance against stress. This study fills the gap in the control of Colletotrichum boninene , which causes leaf spot disease in Sorghum sudangrass hybrids . This paper represents the first reported case of biological control for leaf spot disease in Sorghum sudangrass hybrids and provides a reference for the control of leaf spot disease in Sorghum sudangrass hybrids as well as other crops infected with Colletotrichum boninense .

Fire blight is an infectious disease found in apple and pear orchards. While managing the disease is critical to maintaining orchard health, identifying symptoms early is a challenging task which requires trained expert personnel. This paper presents an inspection technique that targets individual symptoms via deep learning and density estimation. We evaluate the effects of including multi-spectral sensors in the model's pipeline. Results show that adding near infrared (NIR) channels can help improve prediction performance and that density estimation can detect possible symptoms when severity is in the mid-high range.

Poplar ( Populus ) trees play a vital role in various industries and in environmental sustainability. They are widely used for paper production, timber, and as windbreaks, in addition to their significant contributions to carbon sequestration. Given their economic and ecological importance, effective disease management is essential. Convolutional Neural Networks (CNNs), particularly adept at processing visual information, are crucial for the accurate detection and classification of plant diseases. This study introduces a novel dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, enhancing the geographic diversity and application of the dataset. The disease classes consist of "Parsha (Scab)", "Brown-spotting", "White-Gray spotting", and "Rust", reflecting common afflictions in these regions. This dataset will be made publicly available to support ongoing research efforts. Employing the advanced YOLOv8 model, a state-of-the-art CNN architecture, we applied a Contrast Stretching technique prior to model training in order to enhance disease detection accuracy. This approach not only improves the model's diagnostic capabilities but also offers a scalable tool for monitoring and treating poplar diseases, thereby supporting the health and sustainability of these critical resources. This dataset, to our knowledge, will be the first of its kind to be publicly available, offering a valuable resource for researchers and practitioners worldwide.

Given the severe impact of Citrus Huanglongbing on orchard production, accurate detection of the disease is crucial in orchard management. In the natural environments, due to factors such as varying light intensities, mutual occlusion of citrus leaves, the extremely small size of Huanglongbing leaves, and the high similarity between Huanglongbing and other citrus diseases, there remains an issue of low detection accuracy when using existing mainstream object detection models for the detection of citrus Huanglongbing. To address this issue, we propose YOLO-EAF (You Only Look Once-Efficient Asymptotic Fusion), an improved model based on YOLOv8n. Firstly, the Efficient Multi-Scale Attention Module with cross-spatial learning (EMA) is integrated into the backbone feature extraction network to enhance the feature extraction and integration capabilities of the model. Secondly, the adaptive spatial feature fusion (ASFF) module is used to enhance the feature fusion ability of different levels of the model so as to improve the generalization ability of the model. Finally, the focal and efficient intersection over union (Focal-EIOU) is utilized as the loss function, which accelerates the convergence process of the model and improves the regression precision and robustness of the model. In order to verify the performance of the YOLO-EAF method, we tested it on the self-built citrus Huanglongbing image dataset. The experimental results showed that YOLO-EAF achieved an 8.4% higher precision than YOLOv8n on the self-built dataset, reaching 82.7%. The F1-score increased by 3.33% to 77.83%, and the mAP (0.5) increased by 3.3% to 84.7%. Through experimental comparisons, the YOLO-EAF model proposed in this paper offers a new technical route for the monitoring and management of Huanglongbing in smart orange orchards.

Erysiphe alphitoides is a species of powdery mildew responsible for the major foliar disease of oak trees, including Quercus robur . Infection with E. alphitoides leads to a reduction in the growth of the trees and in their ability to survive. This paper reports on the biochemical changes characteristic of defence responses in oak leaves with different infection area sizes, collected in July, August, and September during three growing seasons. The study highlights the effect of E. alphitoides infection on changes in the ascorbate-glutathione cycle, phenolic compound profile, and metal content (mineral distribution). Visible symptoms of pathogen infection appeared gradually in July, but the most intense biochemical plant responses in oak leaves were detected mainly in August and September. These responses included increased ascorbate-glutathione enzyme activities, phenolic compounds, and metal contents. In addition, microscopic analyses revealed a strong fluorescence signal of lignin in the epidermis of pathogen-infected leaves. The involvement of the studied compounds in the basic defence mechanisms of oak against E. alphitoides infection is discussed in the paper.

Chemical-intensive agriculture challenges environmental sustainability and biodiversity and must be changed. Minimizing the use of agrochemicals based on renewable resources can reduce or eliminate ecosystems and biodiversity threats. Nanochitosan as a sustainable alternative offers promising solutions for sustainable agricultural practices that work at multiple spatial and temporal scales throughout the plant growth cycle. This review focuses on the potential of nanochitosan in sustainable agricultural production and provides insights into the mechanisms of action and application options of nanochitosan throughout the plant growth cycle. We emphasize the role of nanochitosan in increasing crop yields, mitigating plant diseases, and reducing agrochemical accumulation. The paper discusses the sources of nanochitosan and its plant growth promotion, antimicrobial properties, and delivery capacity. Furthermore, we outline the challenges and prospects of research trends of nanochitosan in sustainable agricultural production practices and highlight the potential of nanochitosan as a sustainable alternative to traditional agrochemicals. [ABSTRACT FROM AUTHOR]

Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as its backbone and introduces an efficient spatial attention mechanism (effective spatial attention, ESA) to focus on key regions of the images. By replacing the standard convolutions in ResNet34 with depthwise separable convolutions, the model reduces its parameter count by 85.37% and its computational load by 84.51%. Additionally, Dropout is used to mitigate overfitting, and data augmentation techniques such as center cropping and horizontal flipping are employed to enhance the model's robustness. The experimental results show that the improved algorithm achieves an accuracy, precision, and F1 score of 87.09%, 87.14%, and 86.91%, respectively, outperforming several benchmark models (including AlexNet, VGG16, MobileNet, DenseNet, and various ResNet variants). These findings demonstrate that the proposed ESA-ResNet34 model significantly enhances crop pest and disease detection. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of diseases in vegetables is crucial for effective management and mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools for automated disease detection in crops due to their ability to learn intricate patterns from large-scale image datasets and make predictions of samples that are given. The use of CNN algorithms for disease detection in important vegetable crops like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, and cauliflower is critically examined in this review paper. This review examines the most recent state-of-the-art techniques, datasets, and difficulties related to these crops' CNN-based disease detection systems. Firstly, we present a summary of CNN architecture and its applicability to classify tasks based on images. Subsequently, we explore CNN applications in the identification of diseases in vegetable crops, emphasizing relevant research, datasets, and performance measures. Also, the benefits and drawbacks of CNN-based methods, covering problems with computational complexity, model generalization, and dataset size, are discussed. This review concludes by highlighting the revolutionary potential of CNN algorithms in transforming crop disease diagnosis and management strategies. Finally, this study provides insights into the current limitations regarding the usage of computer algorithms in the field of vegetable disease detection. [ABSTRACT FROM AUTHOR]

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification. [ABSTRACT FROM AUTHOR]

This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications. [ABSTRACT FROM AUTHOR]

As one of the world's most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification. [ABSTRACT FROM AUTHOR]

Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately. While traditional neural networks require complete processing of data when processing data, by compressed sensing, only one part of the data needs to be processed, which greatly reduces the amount of data processed by the network. In this paper, a combination of compressed perception and neural networks is used to classify and identify pest images in the compressed domain. A network model for compressed sampling and classification, CSBNet, is proposed to enable compression in neural networks instead of the sensing matrix in conventional compressed sensing (CS). Unlike traditional compressed perception, no reduction is performed to reconstruct the image, but recognition is performed directly in the compressed region, while an attention mechanism is added to enhance feature strength. The experiments in this paper were conducted on different datasets with various sampling rates separately, and our model was substantially less accurate than the other models in terms of trainable parameters, reaching a maximum accuracy of 96.32%, which is higher than the 93.01%, 83.58%, and 87.75% of the other models at a sampling rate of 0.7. [ABSTRACT FROM AUTHOR]

In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.

Citrus is the natural host of at least eight viroid species, providing a natural platform for studying interactions among viroids. The latter manifests as antagonistic or synergistic phenomena. The antagonistic effect among citrus viroids intuitively leads to reduced symptoms caused by citrus viroids, while the synergistic effect leads to an increase in symptom severity. The interaction phenomenon is complex and interesting, and a deep understanding of the underlying mechanisms induced during this viroid interaction is of great significance for the prevention and control of viroid diseases. This paper summarizes the research progress of citrus viroids in recent years, focusing on the interaction phenomenon and analyzing their interaction mechanisms. It points out the core role of the host RNA silencing mechanism and viroid-derived siRNA (vd-siRNA), and provides suggestions for future research directions.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods. [ABSTRACT FROM AUTHOR]

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min. [ABSTRACT FROM AUTHOR]

In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop diseases (BBCPF) was proposed in this paper by utilizing the advantage of knowledge graph in describing complex relations between disease entities in a structured form. This model was composed of two parts, i.e., named-entity recognition and relation extraction, in the form of an assembly line. To deal with the different meanings of Chinese crop disease terms in different contexts and to better obtain the contextual information, the BERT model was introduced for dynamic vector representations. Then, the BiLSTM layer was used to learn long-distance text information, and the CRF was applied to obtain the globally optimal labeling sequence, so as to output the crop disease entities. According to the entity category, the entities were divided as subjects and objects, which were then input into the disordered language model PERT to extract the contextual features of the relation data. At last, the fully connected layer was used to decode the information and output the crop disease entity-relation triples. The experiment results show that, on the self-built disease corpus dataset, the Precision, Recall, and F1-Score values of the established model reached 85.63%, 79.46% and 82.43%, respectively, for entity extraction, and reached 97.96%, 98.43% and 98.16%, respectively, for relation extraction. This paper provides an effective method for information extraction in the construction of Chinese crop disease domain knowledge graphs. [ABSTRACT FROM AUTHOR]

A variety of research reports that compost tea controls plant pathogens and improves plant nutrition and plant growth. Therefore, it can be used to reduce the use of synthetic fertilizers and pesticides. The aim of the study was to characterize and quantify the scientific production in the SCOPUS database on compost tea using bibliometric indicators. A total of 285 published papers related to compost tea were identified. The results show a general increasing trend from 2001 to 2023, with the highest number of publications occurring in 2021. Most of the publications were in the form of original articles, and English was the main language of publication. The top 10 countries with the highest scientific productivity were the United States, Egypt, Spain, Canada, Italy, India, China, Australia, Iran and Malaysia. Zaccardelli, M. and Pane, C. were the authors with the highest productivity with nine articles. In the co-authorship networks, two main networks were registered: the first with Diáñez F., together with Gea F. J., Navarro M.Y. and Santo M., and the second with Zaccardelli M., Celono G., and Pane C. Therefore, the need to adapt more resilient agricultural production systems allows for the consideration of compost tea as an alternative to mitigate environmental problems and soil degradation. [ABSTRACT FROM AUTHOR]

The yield and security of grain are seriously infringed on by crop diseases, which are the critical factor hindering the green and high-quality development of agriculture. The existing crop disease identification models make it difficult to focus on the disease spot area. Additionally, crops with similar disease characteristics are easily misidentified. To address the above problems, this paper proposed an accurate and efficient disease identification model, which not only incorporated local and global features of images for feature analysis, but also improved the separability between similar diseases. First, Transformer Encoder was introduced into the improved model as a convolution operation, so as to establish the dependency between long-distance features and extract the global features of the disease images. Then, Centerloss was introduced as a penalty term to optimize the common cross-entropy loss, so as to expand the inter-class difference of crop disease characteristics and narrow their intra-class gap. Finally, according to the characteristics of the datasets, a more appropriate evaluation index was used to carry out experiments on different datasets. The identification accuracy of 99.62% was obtained on Plant Village, and the balanced accuracy of 96.58% was obtained on Dataset1 with a complex background. It showed good generalization ability when facing disease images from different sources. The improved model also balanced the contradiction between identification accuracy and parameter quantity. Compared with pure CNN and Transformer models, the leaf disease identification model proposed in this paper not only focuses more on the disease regions of leaves, but also better distinguishes different diseases with similar characteristics. [ABSTRACT FROM AUTHOR]

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health. [ABSTRACT FROM AUTHOR]

Simple Summary: Achieving automatic detection of plant diseases in real agricultural scenarios where low-computing-power platforms are deployed is a significant research topic. As fine-grained agriculture continues to expand and farming methods deepen, traditional manual detection methods demand a high labor intensity. In recent years, the rapid advancement of computer network vision has greatly enhanced the computer-processing capabilities for pattern recognition problems across various industries. Consequently, a deep neural network based on an automatic pruning mechanism is proposed to enable high-accuracy plant disease detection even under limited computational power. Furthermore, an application is developed based on this method to expedite the translation of theoretical results into practical application scenarios. Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94. [ABSTRACT FROM AUTHOR]

This paper investigates the usage of machine learning (ML) algorithms on agricultural images with the aim of extracting information regarding the health of plants. More specifically, a custom convolutional neural network is trained on Google Colab using photos of healthy and unhealthy plants. The trained models are evaluated using various single-board computers (SBCs) that demonstrate different essential characteristics. Raspberry Pi 3 and Raspberry Pi 4 are the current mainstream SBCs that use their Central Processing Units (CPUs) for processing and are used for many applications for executing ML algorithms based on popular related libraries such as TensorFlow. NVIDIA Graphic Processing Units (GPUs) have a different rationale and base the execution of ML algorithms on a GPU that uses a different architecture than a CPU. GPUs can also implement high parallelization on the Compute Unified Device Architecture (CUDA) cores. Another current approach involves using a Tensor Processing Unit (TPU) processing unit carried by the Google Coral Dev TPU Board, which is an Application-Specific Integrated Circuit (ASIC) specialized for accelerating ML algorithms such as Convolutional Neural Networks (CNNs) via the usage of TensorFlow Lite. This study experiments with all of the above-mentioned devices and executes custom CNN models with the aim of identifying plant diseases. In this respect, several evaluation metrics are used, including knowledge extraction time, CPU utilization, Random Access Memory (RAM) usage, swap memory, temperature, current milli Amperes (mA), voltage (Volts), and power consumption milli Watts (mW). [ABSTRACT FROM AUTHOR]

This paper studies a particular type of planar Filippov system that consists of two discontinuity boundaries separating the phase plane into three disjoint regions with different dynamics. This type of system has wide applications in various subjects. As an illustration, a plant disease model and an avian-only model are presented, and their bifurcation scenarios are investigated. By means of the regularization approach, the blowing up method, and the singular perturbation theory, we provide a different way to analyze the dynamics of this type of Filippov system. In particular, the boundary equilibrium bifurcations of such systems are studied. As a consequence, the nonsmooth fold bifurcation becomes a saddle-node bifurcation, while the persistence bifurcation disappears after regularization. [ABSTRACT FROM AUTHOR]

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α -IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches. [ABSTRACT FROM AUTHOR]

Background: Plant-based remedies have been used since antiquity to treat menstrual-related diseases (MD). From the late nineteenth to the early to mid-twentieth century, Italian folk remedies to treat "women's diseases" were documented in a vast corpus of literature sources. Aim: The purpose of this paper is to bring to light the plant-based treatments utilized by Italian folk medicine to heal clinical manifestations of premenstrual syndrome (PMS), dysmenorrhea, amenorrhea and menstrual disorders in an attempt to discuss these remedies from a modern pharmacological point of view. Moreover, we compare the medical applications described by Hippocrates with those utilized by Italian folk medicine to check if they result from a sort of continuity of use by over two thousand years. Results: Out of the 54 plants employed in Italian folk medicine, 25 (46.3%) were already documented in the pharmacopoeia of the Corpus Hippocraticum for treating MD. Subsequently, a detailed search of scientific data banks such as Medline and Scopus was undertaken to uncover recent results concerning bioactivities of the plant extracts to treat MD. About 26% of the plants used by Italian folk medicine, nowadays, have undergone human trials to assess their actual efficacy. At the same time, about 41% of these herbal remedies come back to in different countries. Conclusions: Active principles extracted from plants used by Italian folk healers could be a promising source of knowledge and represent strength candidates for future drug discovery for the management of MD. [ABSTRACT FROM AUTHOR]

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. [ABSTRACT FROM AUTHOR]

Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system. [ABSTRACT FROM AUTHOR]

It is of great practical significance to quickly, accurately, and effectively identify the effects of rice diseases on rice yield. This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method uses DenseNet as the benchmark model and uses the channel attention mechanism squeeze-and-excitation to strengthen the favorable features, while suppressing the unfavorable features. Then, depth wise separable convolutions are introduced to replace some standard convolutions in the dense network to improve the parameter utilization and training speed. Using the AdaBound algorithm, combined with the adaptive optimization method, the parameter adjustment time reduces. In the experiments on five kinds of rice disease datasets, the average classification accuracy of the method in this paper is 99.4%, which is 13.8 percentage points higher than the original model. At the same time, it is compared with other existing recognition methods, such as ResNet, VGG, and Vision Transformer. The recognition accuracy of this method is higher, realizes the effective classification of rice disease images, and provides a new method for the development of crop disease identification technology and smart agriculture. [ABSTRACT FROM AUTHOR]