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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. [ABSTRACT FROM AUTHOR]

Potato is one of the most nutritious foods rich in fiber and essential vitamins and minerals. However, potato plants may suffer from diseases such as early blight, late blight, septoria leaf spot or skin diseases such as greening, silver scurf, common scab, black rot etc. These diseases may lead to huge losses in the potato yield annually. Hence, an early prediction of such diseases is required so that they can be controlled at an early stage. Several machine learning, IoT based and image processing techniques have been utilized for this purpose, and this paper aims to survey all such methods and prediction models. A total of 116 studies were analyzed, and out of them, 77 relevant studies belonging to (60%) journals, (34%) conferences and (6%) other sources have been surveyed. It has been observed that most of the selected studies (64%) have used RGB image data (proprietary or PlantVillage dataset). Also, majority of the studies (48%) have worked with imbalanced data. Late blight and early blight are the two diseases that have gained the most attention by the researchers, being included in 58% and 34% of the studies. Moreover, feature extraction/selection has been used only with image data (RGB and hyperspectral). Convolutional Neural Network is the most widely used classifier, followed by Support Vector Machines. This review will help the researchers gain an insight into the work done for disease detection in potato crop and what techniques or diseases need intervention so that yield losses can further be minimized. [ABSTRACT FROM AUTHOR]

Climate change has facilitated the introduction, establishment, and movement of invasive species in northern regions, enabling the colonization of previously unsuitable areas. While the responses of insects to these changes have been increasingly studied, our understanding of how such alterations impact trophic interactions still requires further research to make reliable predictions about the spread of diseases in a warming world. Phytoplasmas, a group of obligate parasitic unculturable Mollicutes, primarily rely on leafhoppers (Hemiptera: Cicadellidae) for transmission, spread, and survival. Phytoplasmas are associated with over 600 diseases affecting more than 1,000 plant species, including berries, grapevines, and other small fruits. In North America, diseases such as grapevine yellows, blueberry stunt, and strawberry green petal diseases have been linked to phytoplasma strains transmitted by known leafhopper species. However, the number of phytoplasma diseases has significantly increased in North America over the past decade, suggesting the presence of unidentified vectors or an abundance of leafhopper vectors. This short review provides an overview of the current knowledge on leafhoppers as vectors of phytoplasmas to berries, focusing on the last decade's research in Canada. This paper also explores the potential implications of climate change on this pathosystem, including the anticipated range expansion of leafhopper species, changes in phytoplasma acquisition and transmission, and the risk of new leafhopper-transmitted plant-pathogen introductions through the arrival of new leafhopper species. [ABSTRACT FROM AUTHOR]

The Main Aim of this project is to identify the Green and effected leaf with the help of raspberry pi and agricultural sensors. This study addressed a framework for detecting and preventing the transmission of plant disease using Raspberry PI. For image processing, the k means clustering algorithm was used. It has a variety of focus points for use in large harvest ranches, and it naturally distinguishes signs of illness anywhere they appear on plant leaves. The recognition of leaf disease is a key theme in pharmaceutical science since it has the benefits of tracking crops in the field in the form and thereby automatically identifies signs of disease by image processing using a clustering k-means algorithm. The word "disease" describes the form of plant damage that occurs. This paper demonstrates the most effective technique for detecting plant infections using the ideas of image processing and alerting the disease name through e-mail, SMS text, and presenting the disease name on the framework proprietor's screen monitor. Disease signs may be detected automatically, which is helpful for improving agricultural goods. The chemical application would benefit greatly from fully automated design and deployment of these technologies. Pesticides and other ingredients would be less expensive. Farm production will rise as a result of this. This system has usb camera, temperature, humidity sensor, fertilizer, GSM and Raspberry pi. All these sensors are interfaced to the raspberry pi. This system continuously monitors the all the environment conditions and display on monitor. If effected leaf detected at any place, then this system automatically sprays the fertilizer on crop. So that this system makes better and user friendly system for formers. [ABSTRACT FROM AUTHOR]

The unprecedented worldwide spread of the Citrus greening disorder, called Huanglongbing (HLB), has urged researchers for rapid interventions. HLB poses a considerable threat to global citriculture owing to its devastating impact on citrus species. This disease is caused by Candidatus Liberibacter species (CLs), primarily transferred through psyllid insects, such as Trioza erytreae and Diaphorina citri. It results in phloem malfunction, root decline, and altered plant source-sink relationships, leading to a deficient plant with minimal yield before it dies. Thus, many various techniques have been employed to eliminate HLB and control vector populations through the application of insecticides and antimicrobials. The latter have evidenced short-term efficiency. While nucleic acid-based analyses and symptom-based identification of the disease have been used for detection, they suffer from limitations such as false negatives, complex sample preparation, and high costs. To address these challenges, secreted protein-based biomarkers offer a promising solution for accurate, rapid, and cost-effective disease detection. This paper presents an overview of HLB symptoms in citrus plants, including leaf and fruit symptoms, as well as whole tree symptoms. The differentiation between HLB symptoms and those of nutrient deficiencies is discussed, emphasizing the importance of precise identification for effective disease management. The elusive nature of CLs and the challenges in culturing them in axenic cultures have hindered the understanding of their pathogenic mechanisms. However, genome sequencing has provided insights into CLs strains' metabolic traits and potential virulence factors. Efforts to identify potential host target genes for resistance are discussed, and a high-throughput antimicrobial testing method using Citrus hairy roots is introduced as a promising tool for rapid assessment of potential treatments. This review summarizes current challenges and novel therapies for HLB disease. It highlights the urgency of developing accurate and efficient detection methods and identifying the complex relations between CLs and their host plants. Transgenic citrus in conjunction with secreted protein-based biomarkers and innovative testing methodologies could revolutionize HLB management strategies toward achieving a sustainable citrus cultivation. It offers more reliable and practical solutions to combat this devastating disease and safeguard the global citriculture industry. • Huanglongbing (HLB) poses a considerable threat to global citriculture. • HLB caused by Candidatus Liberibacter species and transferred by psyllid insects. • Pesticides, antimicrobial, transgenic plants, thermotherapy, etc. Can control HLB. • Combination of different techniques could revolutionize HLB management. [ABSTRACT FROM AUTHOR]