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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]

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]

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]

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]

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]

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]

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. [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]

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]

Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0′s feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model. [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]

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]

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.7%, with MobileNetv2. Based on the results obtained, an offline mobile application was developed, supported on devices with Android 4.1 or later, also featuring an information section on the 27 diseases affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of diseases affecting the same plant. [ABSTRACT FROM AUTHOR]

Phages of phytopathogenic bacteria are considered to be promising agents for the biological control of bacterial diseases in plants. This paper reports on the isolation and characterisation of a new Xanthomonas campestris pv. campestris phage, Murka. Phage morphology and basic kinetic characteristics of the infection were determined, and a phylogenomic analysis was performed. The phage was able to lyse a reasonably broad range (64%, 9 of the 14 of the Xanthomonas campestris pv. campestris strains used in the study) of circulating strains of the cabbage black rot pathogen. This lytic myovirus has a DNA genome of 44,044 bp and contains 83 predicted genes. Taxonomically, it belongs to the genus Foxunavirus. This bacteriophage is promising for use as a possible means of biological control of cabbage black rot. [ABSTRACT FROM AUTHOR]

One of the main obstacles in rice cultivation is tungro disease, caused by Rice Tungro Spherical Virus (RTSV) and Rice Tungro Bacilliform Virus (RTBV), which are transmitted by green leafhopper vectors (Nephotettix virescens). This disease can be controlled by using pesticides and refugia plants. Excessive use of pesticides can have negative impacts and high costs, so it is necessary to control the use of pesticides. In this study, a mathematical model of the spread of tungro virus disease in rice plants was developed by considering the characteristics of the virus, the presence of green leafhoppers and natural enemies, refugia planting, and pesticide use. From this model, dynamic and sensitivity analyses were carried out, and the optimal control theory was searched using the Pontryagin minimum principle. The analysis results showed three equilibriums: two non-endemic equilibriums (when plant and vector populations exist and when plant, vector, and natural enemy populations exist) and one endemic equilibrium. The non-endemic equilibrium will be asymptotically stable locally if R 0 < 1 . At the same time, the parameters that greatly influence the spread of this disease are parameters μ ,   μ 2 , and ϕ for local sensitivity analysis and α ,   a ,   β ,   b ,   ϕ , and μ 2 for global sensitivity analysis. The results of the numerical simulation show that control using combined control is more effective in reducing the intensity of the spread of tungro disease in rice plants than control in the form of planting refugia plants as a source of food for natural enemies. The use of pesticides is sufficient for only four days, so the costs incurred are quite effective in controlling the spread of this disease. [ABSTRACT FROM AUTHOR]

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVIRB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVIRB was designed. GRVIRB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVIRB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales. [ABSTRACT FROM AUTHOR]

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as "Jordan22" was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. [ABSTRACT FROM AUTHOR]

Pythium spp. are soil-borne pathogens that cause damping-off and root rot diseases in many plant species such as cucumber. In the current study, the effect of dried roots–stems and leaves of Ageratina adenophora (Sprengel) R. King and H. Robinson (=Eupatorium adenophorum Sprengel) alone and in combination with pyrogenic biomass biochar to control Pythium aphanidermatum (Edson) Fitz was assessed. In four treatments of leaves, roots–stems, leaves + biochar, and roots–stems + biochar, it was observed that the treatment with leaves at an Emax (maximal effective concentration on control fungi) of 79 g/kg of soil had the most antifungal effect on P. aphanidermatum. Also, the Cmax (the highest level of control) increased with time and reached 82.4% and 71% on days 30 and 60, respectively. The highest cucumber fresh fruit weight and the highest height of the stems in the greenhouse were observed in leaf treatment of A. adenophora. Biochar did not have any remarkable controlling effect on P. aphanidermatum, and its population increased. The main compounds extracted from the dried leaves and roots–stems of A. adenophora, including α-pinene, nonanone, hexahydronaphthalene, 3-undecanone, muurolene, and heneicosane, had antifungal properties. We concluded that the leaves of A. adenophora have the potential to be used as a bio-fumigant for P. aphanidermatum management. [ABSTRACT FROM AUTHOR]

Septoria leaf spot (SLS), caused by the hemibiotrophic fungus Septoria lycopersici, poses a significant threat to tomato production systems. While fungicides can effectively reduce disease epidemics, planting resistant cultivars remains the most efficient and economical control method. Conventional screening and breeding have identified new sources of resistance to S. lycopersici among wild relatives. However, gaps exist in the literature concerning tomato—S. lycopersici interactions, such as plant defense mechanisms, fungal pathogenicity mechanisms, and interaction dynamics that can inform the development of durable resistance through additional research. To further enhance host resistance to S. lycopersici, molecular methods such as marker-assisted breeding, gene editing, genomic selection, and transgenic approaches can be employed. Moreover, the durability of resistance and efficacy of disease management can be optimized within an integrated pest management (IPM) framework that advances diverse tactics to suppress diseases and improve plant productivity. This review highlights the current understanding of pathogen biology, host genetic solutions, and novel strategies to combat the SLS problem and suggests directions for future research. [ABSTRACT FROM AUTHOR]

This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet's appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications. [ABSTRACT FROM AUTHOR]

A pathogen strain responsible for sweet potato stem and foliage scab disease was isolated from sweet potato stems. Through a phylogenetic analysis based on the rDNA internal transcribed spacer (ITS) region, combined with morphological methods, the isolated strain was identified as Elsinoë batatas. To comprehensively analyze the pathogenicity of the isolated strain from a genetic perspective, the whole-genome sequencing of E. batatas HD-1 was performed using both the PacBio and Illumina platforms. The genome of E. batatas HD-1 is about 26.31 Mb long in 167 scaffolds, with a GC content of 50.81%, and 7898 protein-coding genes, 131 non-coding RNAs, and 1954 interspersed repetitive sequences were predicted. Functional annotation revealed that 408 genes encode virulence factors involved in plant disease (DFVF—Plant). Notably, twenty-eight of these virulence genes encode secretory carbohydrate-active enzymes (CAZymes), including two endo-1,4-β-xylanase genes and seven cutinase genes, which suggested that endo-1,4-β-xylanase and cutinase play a vital role in the pathogenicity of E. batatas HD-1 within sweet potato. In total, twelve effectors were identified, including five LysM effectors and two CDIP effectors, suggesting that LysM and CDIP effectors play significant roles in the interaction between E. batatas HD-1 and sweet potato. Additionally, our analysis of biosynthetic gene clusters (BGCs) showed that two gene clusters are involved in melanin and choline metabolism. This study enriches the genomic resources of E. batatas and provides a theoretical foundation for future investigations into the pathogenic mechanisms of its infection in sweet potatoes, as well as potential targets for disease control. [ABSTRACT FROM AUTHOR]

Closed environment agriculture (CEA) is rapidly gaining traction as a sustainable option to meet global food demands while mitigating the impacts of climate change. Fungal pathogens represent a significant threat to crop productivity in CEA, where the controlled conditions can inadvertently foster their growth. Historically, the detection of pathogens has largely relied on the manual observation of signs and symptoms of disease in the crops. These approaches are challenging at large scale, time consuming, and often too late to limit crop loss. The emergence of fungicide resistance further complicates management strategies, necessitating the development of more effective diagnostic tools. Recent advancements in technology, particularly in molecular and isothermal diagnostics, offer promising tools for the early detection and management of fungal pathogens. Innovative detection methods have the potential to provide real-time results and enhance pathogen management in CEA systems. This review explores isothermal amplification and other new technologies in detection of fungal pathogens that occur in CEA. [ABSTRACT FROM AUTHOR]

This study investigated the biosynthesis, statistical optimization, characterization, and biocontrol activity of silver nanoparticles (AgNPs) produced by newly isolated Trichoderma sp. The Trichoderma asperellum strain TA-3N was identified based on the ITS gene sequence, together with its phenotypic characteristics (GenBank accession number: OM321439). The color change from light yellow to brown after the incubation period indicates AgNPs biosynthesis. The UV spectrum revealed a single peak with the maximum absorption at 453 nm, indicating that T. asperellum produces AgNPs effectively. A Rotatable Central Composite Design (RCCD) was used to optimize the biosynthesis of AgNPs using the aqueous mycelial-free filtrate of T. asperellum. The optimal conditions for maximum AgNPs biosynthesis (156.02 µg/mL) were predicted theoretically using the desirability function tool and verified experimentally. The highest biosynthetic produced AgNPs by T. asperellum reached 160.3 µg/mL using AgNO3 concentration of 2 mM/mL, initial pH level of 6, incubation time of 60 h, and biomass weight of 6 g/100 mL water. SEM and TEM imaging revealed uniform spherical shape particles that varied in size between 8.17 and 17.74 nm. The synthesized AgNPs have a Zeta potential value of −9.51 mV. FTIR analysis provided insights into the surface composition of AgNPs, identifying various functional groups such as N–H, -OH, C-H, C=O, and the amide I bond in proteins. Cytotoxicity and genotoxicity assays demonstrated that AgNPs in combination with T. asperellum can mitigate the toxic effects of Fusarium oxysporum on barley. This intervention markedly enhanced cell division rates and decreased chromosomal irregularities. The results indicate that AgNPs synthesized by T. asperellum show the potential as an eco-friendly and efficient method for controlling plant diseases. Further studies are necessary to investigate their possible use in the agricultural sector. [ABSTRACT FROM AUTHOR]

Movements of plant pathogenic microorganisms in uncontaminated areas occur today at an alarming rate, driven mainly by global trade and climate change. These invaders can trigger new disease outbreaks able to impact the biodiversity and economies of vast territories and affect a variety of ecosystem services. National and supranational regulatory deficiencies, such as inadequate quarantine measures and ineffective early pathogen detection at ports of entry, exacerbate the issue. Thus, there is an urgent need for accurate and rapid diagnostic tools to intercept invasive and nonindigenous plant pathogens. The LAMP (Loop-mediated isothermal AMPlification) technique is a robust, flexible tool representing a significant advance in point-of-care (POC) diagnostics. Its user-friendliness and sensitivity offer a breakthrough in phytosanitary checks at points of entry (harbors and airports), for disease and pest surveillance at vulnerable sites (e.g., nurseries and wood-processing and storage facilities), and for territorial monitoring of new disease outbreaks. This review highlights the strengths and weaknesses of LAMP, emphasizing its potential to revolutionize modern plant disease diagnostics. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is an aerobic, Gram-negative bacterium that is responsible for many plant diseases. The bacterium is the causal agent of Pierce's disease in grapes and is also responsible for citrus variegated chlorosis, peach phony disease, olive quick decline syndrome and leaf scorches of various species. The production of biofilm is intrinsically linked with persistence and transmission in X. fastidiosa. Biofilm formation is regulated by members of the Diffusible Signal Factor (DSF) quorum sensing signalling family which are comprised of a series of long chain cis-unsaturated fatty acids. This article describes the evaluation of a library of N-acyl sulfonamide bioisosteric analogues of BDSF, XfDSF1 and XfDSF2 for their ability to control biofilm production in X. fastidiosa. The compounds were screened against both the wild-type strain Temecula and an rpfF* mutant which can perceive but not produce XfDSF. Planktonic cell abundance was measured via OD600 while standard crystal violet assays were used to determine biofilm biomass. Several compounds were found to be effective biofilm inhibitors depending on the nature of the sulfonamide substituent. The findings reported here may provide future opportunities for biocontrol of this important plant pathogen. [ABSTRACT FROM AUTHOR]

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification and timely management of these issues. Existing models for tomato leaf disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) and Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global feature recognition; conversely, VTs are advantageous for global feature extraction but are less effective at capturing local features. This discrepancy hampers the performance improvement of both model types in the task of tomato leaf disease identification. Currently, effective fusion models that combine CNNs and VTs are still relatively scarce. We developed an efficient CNNs and VTs fusion network named ECVNet for tomato leaf disease recognition. Specifically, we first designed a Channel Attention Residual module (CAR module) to focus on channel features and enhance the model's sensitivity to the importance of feature channels. Next, we created a Convolutional Attention Fusion module (CAF module) to effectively extract and integrate both local and global features, thereby improving the model's spatial feature extraction capabilities. We conducted extensive experiments using the Plant Village dataset and the AI Challenger 2018 dataset, with ECVNet achieving state-of-the-art recognition performance in both cases. Under the condition of 100 epochs, ECVNet achieved an accuracy of 98.88% on the Plant Village dataset and 86.04% on the AI Challenger 2018 dataset. The introduction of ECVNet provides an effective solution for the identification of plant leaf diseases. [ABSTRACT FROM AUTHOR]

Yellow rose (Rosa xanthina) is a common ornamental shrub species widely cultivated in China. However, canker disease symptoms were discovered during our investigations in Beijing and Xinjiang, China. The fungal isolates were obtained from diseased barks and identified using combined methods of morphology and phylogeny based on a partial region of ITS, LSU, rpb2, tef1, and tub2 sequences. As a result, a new species of Seimatosporium named S. chinense was proposed and described herein. The new species is distinguished from its phylogenetic sister species, S. gracile and S. nonappendiculatum, by conidial characters. The present study improves the species concept in Seimatosporium and provides fundamental data for the yellow rose canker disease control in the future. [ABSTRACT FROM AUTHOR]

Research into the biology of plant viruses, their mechanisms of pathogenicity, and the induction of host resistance has laid a solid foundation for the discovery of antiviral agents and their targets and the development of effective control technologies. Additionally, recent advancements in fields such as chemical biology, cheminformatics, bioinformatics, and synthetic biology have provided valuable methods and tools for the design of antiviral drugs, the synthesis of drug molecules, assessment of their activity, and investigation of their modes of action. Compared with drug development for human viral diseases, the control of plant viral diseases presents greater challenges, including the cost-benefit of agents, simplification of control technologies, and the effectiveness of treatments. Therefore, in the current context of complex outbreaks and severe damage caused by plant viral diseases, it is crucial to delve deeper into the research and development of antiviral agents. This review provides a detailed overview of the biological characteristics of current targets for antiviral agents, the mode of interaction between plant virus targets and antivirals, and insights for future drug development. We believe this review will not only facilitate the in-depth analysis of the development of antivirals for crops but also offer valuable perspectives for the development of antiviral agents for use in human and veterinary medicine. [ABSTRACT FROM AUTHOR]

During a survey conducted in autumn 2022 and spring 2023, extensive leaf spots were observed on Aristolochia grandiflora plants in the Botanical Garden "Angelo Rambelli" (Viterbo). To preserve the botanical garden collection and avoid disease spread, morphological and molecular identification of the causal agent were carried out. The results revealed three distinct Colletotrichum species belonging to the Colletotrichum boninense and Colletotrichum orchidearum species complexes, which have never been reported together within the same host and, for two of them, never in Italy. These findings may contribute to further extend the state of the art on leaf anthracnose, as well as provide new insights and molecular data for further phylogenetic studies. [ABSTRACT FROM AUTHOR]

Cover crops, typically planted during off-seasons and requiring less agronomic manipulation, may provide abundant fungal resources. Certain species of Chaetomiaceae could serve as potential agents for controlling plant diseases and developing bioorganic fertilizers. Eight species from five genera of Chaetomiaceae were identified from healthy Astragalus sinicus and Vicia villosa, two major cover crops, through multigene phylogenetic analysis, morphological identification, and pairwise homoplasy index testing. The identified species comprise three new species: Achaetomium astragali, Subramaniula henanensis, and S. sichuanensis, as well as five known but new host record species: Botryotrichum murorum, Chaetomium coarctatum, C. pseudocochliodes, C. pseudoglobosum, and Collariella pachypodioides. Dual culture tests revealed that isolates of all eight Chaetomiaceae species exhibited antagonistic effects on multiple phytopathogens. Among the identified fungi, the NSJA2 isolate, belonging to C. coarctatum, exhibited significant relative inhibition effects on 14 out of 15 phytopathogens tested in this study, indicating its broad-spectrum antagonistic effects. Additionally, NSJA2 exhibited excellent salt tolerance. Overall, our study has identified multiple fungi with anti-phytopathogens potential, among which NSJA2 exhibits high potential for practical application. This finding paves the way for further exploration and exploitation of NSJA2 as a promising biocontrol agent. [ABSTRACT FROM AUTHOR]

Phytophthora-induced crop diseases, commonly known as "plant plagues", pose a significant threat to global food security. In this study, strain ASG80 was isolated from sisal roots and demonstrated a broad-spectrum antagonistic activity against several Phytophthora species and fungal pathogens. Strain ASG80 was identified as Streptomyces luteireticuli via phylogenetic analysis, digital DNA–DNA hybridization (dDDH), and average nucleotide identity (ANI). Whole-genome sequencing identified 40 biosynthetic gene clusters (BGCs) related to secondary metabolite production, including antimicrobial compounds. Strain ASG80 extract exhibited broad-spectrum inhibitory activity against Phytophthora nicotianae, P. vignae, P. cinnamomi, and P. sojae. Pot experiments showed that strain ASG80 extract significantly reduced sisal zebra disease incidence, with an efficacy comparable to the fungicide metalaxyl. These findings suggest that strain ASG80 is a promising biocontrol agent with substantial potential for managing Phytophthora-related diseases in agriculture. [ABSTRACT FROM AUTHOR]

Bacterial wilt triggered by the soil-borne pathogenic bacterium Ralstonia solanacearum is one of the most serious diseases in tomato plants, leading to huge economic losses worldwide. Biological control is considered an environmentally friendly and sustainable way to manage soil-borne diseases. In this study, Streptomyces sp. STD57 isolated from the rhizosphere of Adenophora stricta showed strong antibacterial activity against R. solanacearum. Pot experiments showed that strain STD57 exhibited a significant biocontrol effect (81.7%) on tomato bacterial wilt in the greenhouse environment. Furthermore, strain STD57 could inhibit the growth of weeds (Amaranthus retroflexus, Portulaca oleracea, and Echinochloa crusgalli) but promote the growth of crops (wheat, rice, and tomato). The plant growth-promoting substance was identified as indoleacetic acid (IAA) by high-pressure liquid chromatography–mass spectrometry and genome analysis. Coarse separation of the fermented extracts revealed that the antibacterial and herbicidal substances were mainly in the fermentation supernatant and belonged to different products. These findings suggested that strain STD57 may be a potential biocontrol and bioherbicide agent useful in agriculture. [ABSTRACT FROM AUTHOR]

Agriculture is dealing with numerous challenges of increasing production while decreasing the amount of chemicals and fertilizers used. The intensification of agricultural systems has been linked to the use of these inputs which nevertheless have negative consequences for the environment. With new technologies, and progress in precision agriculture associated with decision support systems for farmers, the objective is to optimize their use. This review focused on the progress made in utilizing machine learning and remote sensing to detect and identify crop diseases that may help farmers to (i) choose the right treatment, the most adapted to a particular disease, (ii) treat diseases at early stages of contamination, and (iii) maybe in the future treat only where it is necessary or economically profitable. The state of the art has shown significant progress in the detection and identification of disease at the leaf scale in most of the cultivated species, but less progress is done in the detection of diseases at the field scale where the environment is complex and applied only in some field crops. [ABSTRACT FROM AUTHOR]

Phytopathogens are a significant challenge to agriculture and food security. In this regard, methods for the early diagnosis of plant diseases, including optical methods, are being actively developed. This review focuses on one of the optical diagnostic methods, chlorophyll fluorescence (ChlF) imaging. ChlF reflects the activity of photosynthetic processes and responds subtly to environmental factors, which makes it an excellent tool for the early detection of stressors, including the detection of pathogens at a pre-symptomatic stage of disease. In this review, we analyze the peculiarities of changes in ChlF parameters depending on the type of pathogen (viral, bacterial, or fungal infection), the terms of disease progression, and its severity. The main mechanisms responsible for the changes in ChlF parameters during the interaction between pathogen and host plant are also summarized. We discuss the advantages and limitations of ChlF imaging in pathogen detection compared to other optical methods and ways to improve the sensitivity of ChlF imaging in the early detection of pathogens. [ABSTRACT FROM AUTHOR]

Tomato (Solanum lycopersicum L.) production is constantly threatened by several fungal pathogens, such as Alternaria solani, the causal agent of early blight disease. In this study, a greenhouse experiment was set up to evaluate the biocontrol ability of arbuscular mycorrhizal fungi (AMF) against A. solani in the presence of reduced doses of fungicides (i.e., captan and copper oxychloride). Disease severity, plant growth traits, chlorophyll and phosphorus content, phenolic compounds, and antioxidant activity were assessed. The effects of fungicide dose on AMF were investigated by root colonization, spore density, and mycorrhizal dependence evaluation. AMF-inoculated and fungicide-treated plants reduced disease severity compared to fungicide-treated and non-mycorrhizal plants, in most cases, regardless of the fungicide dose. AMF improved plant growth, especially when combined with copper oxychloride. However, plant fresh weight decreased in plants treated with the lowest dose of captan (25 g 100 L−1). Overall, AMF colonization decreased in plants with high fungicide doses, while the leaf color parameters did not show differences between treatments. The results suggest reducing the fungicide dose using AMF is possible, particularly for copper oxychloride. Further studies will be required to confirm these data. This integrated approach could offer a sustainable alternative to decrease the use of chemical control. [ABSTRACT FROM AUTHOR]