Addressing plant diseases and pests is critical for enhancing crop production and preventing economic losses. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have significantly improved the precision and efficiency of detection methods, surpassing the limitations of manual identification. This study reviews modern computer-based techniques for detecting plant diseases and pests from images, including recent AI developments. The methodologies are organized into five categories: hyperspectral imaging, non-visualization techniques, visualization approaches, modified deep learning architectures, and transformer models. This structured taxonomy provides researchers with detailed, actionable insights for selecting advanced state-of-the-art detection methods. A comprehensive survey of recent work and comparative studies demonstrates the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in speed and accuracy. In particular, vision transformers such as the Hierarchical Vision Transformer (HvT) have shown accuracy exceeding 99.3% in plant disease detection, outperforming architectures like MobileNetV3. The study concludes by discussing system design challenges, proposing solutions, and outlining promising directions for future research.

Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality. Early detection and classification of these diseases are essential for minimising losses and improving crop management practices. This study applies Convolutional Neural Networks (CNN) and Long Short - Term Memory (LSTM) models to classify plant leaf diseases usin g a dataset containing 70,295 training images and 17,572 validation images across 38 disease classes. The CNN model was trained using the Adam optimiser with a learning rate of 0.0001 and categorical cross - entropy as the loss function. After 10 training ep ochs, the model achieved a training accuracy of 99.1% and a validation accuracy of 96.4%. The LSTM model reached a validation accuracy of 93.43%. Performance was evaluated using precision, recall, F1 - score, and confusion matrix, confirming the reliability of the CNN - based approach. The results suggest that deep learning models, particularly CNN, enable an effective solution for accurate and scalable plant disease classification, supporting practical applications in agricultural monitoring .

Plant diseases significantly threaten global food security by reducing crop yields and undermining agricultural sustainability. AI-driven automated classification has emerged as a promising solution, with deep learning models demonstrating impressive performance in plant disease identification. However, deploying these models on mobile and edge devices remains challenging due to high computational demands and resource constraints, highlighting the need for lightweight, accurate solutions for accessible smart agriculture systems. To address this, we propose MobilePlantViT, a novel hybrid Vision Transformer (ViT) architecture designed for generalized plant disease classification, which optimizes resource efficiency while maintaining high performance. Extensive experiments across diverse plant disease datasets of varying scales show our model's effectiveness and strong generalizability, achieving test accuracies ranging from 80% to over 99%. Notably, with only 0.69 million parameters, our architecture outperforms the smallest versions of MobileViTv1 and MobileViTv2, despite their higher parameter counts. These results underscore the potential of our approach for real-world, AI-powered automated plant disease classification in sustainable and resource-efficient smart agriculture systems. All codes will be available in the GitHub repository: https://github.com/moshiurtonmoy/MobilePlantViT

A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it's too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we've mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle is used, which has 17,430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.

The robot uses its chest, hips and arms to handle objects -- just like we do. Theo works weekdays, weekends and nights and never complains about a sore spine despite performing hour upon hour of what, for a regular farm hand, would be backbreaking labor checking Dutch tulip fields for sick flowers. The boxy robot -- named after a retired employee at the WAM Pennings farm near the Dutch North Sea coast -- is a new high-tech weapon in the battle to root out disease from the bulb fields as they erupt into a riot of springtime color. On a windy spring morning, the robot trundled Tuesday along rows of yellow and red "goudstuk" tulips, checking each plant and, when necessary, killing diseased bulbs to prevent the spread of the tulip-breaking virus. The dead bulbs are removed from healthy ones in a sorting warehouse after they have been harvested.

Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields.

Crops hold paramount significance as they serve as the primary provider of energy, nutrition, and medicinal benefits for the human population. Plant diseases, however, can negatively affect leaves during agricultural cultivation, resulting in significant losses in crop output and economic value. Therefore, it is crucial for farmers to identify crop diseases. However, this method frequently necessitates hard work, a lot of planning, and in-depth familiarity with plant pathogens. Given these numerous obstacles, it is essential to provide solutions that can easily interface with mobile and IoT devices so that our farmers can guarantee the best possible crop development. Various machine learning (ML) as well as deep learning (DL) algorithms have been created & studied for the identification of plant disease detection, yielding substantial and promising results. This article presents a novel classification method that builds on prior work by utilising attention-based feature extraction, RGB channel-based chromatic analysis, Support Vector Machines (SVM) for improved performance, and the ability to integrate with mobile applications and IoT devices after quantization of information. Several disease classification algorithms were compared with the suggested model, and it was discovered that, in terms of accuracy, Vision Transformer-based feature extraction and additional Green Chromatic Coordinate feature with SVM classification achieved an accuracy of (GCCViT-SVM) - 99.69%, whereas after quantization for IoT device integration achieved an accuracy of - 97.41% while almost reducing 4x in size. Our findings have profound implications because they have the potential to transform how farmers identify crop illnesses with precise and fast information, thereby preserving agricultural output and ensuring food security.

This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.

Lesion detection on plant leaves is a critical task in plant pathology and agricultural research. Identifying lesions enables assessing the severity of plant diseases and making informed decisions regarding disease control measures and treatment strategies. To detect lesions, there are studies that propose well-known object detectors. However, training object detectors to detect small objects such as lesions can be problematic. In this study, we propose a method for lesion detection on plant leaves utilizing class activation maps generated by a ResNet-18 classifier. In the test set, we achieved a 0.45 success rate in predicting the locations of lesions in leaves. Our study presents a novel approach for lesion detection on plant leaves by utilizing CAMs generated by a ResNet classifier while eliminating the need for a lesion annotation process.

=One of the most frequently farmed crops is the tomato crop. Late blight is the most prevalent tomato disease in the world, and often causes a significant reduction in the production of tomato crops. The importance of tomatoes as an agricultural product necessitates early detection of late blight. It is produced by the fungus Phytophthora. The earliest signs of late blight on tomatoes are unevenly formed, water-soaked lesions on the leaves located on the plant canopy's younger leave White cottony growth may appear in humid environments evident on the undersides of the leaves that have been impacted. Lesions increase as the disease proceeds, turning the leaves brown to shrivel up and die. Using picture segmentation and the Multi-class SVM technique, late blight disorder is discovered in this work. Image segmentation is employed for separating damaged areas on leaves, and the Multi-class SVM method is used for reliable disease categorization. 30 reputable studies were chosen from a total of 2770 recognized papers. The primary goal of this study is to compile cutting-edge research that identifies current research trends, problems, and prospects for late blight detection. It also looks at current approaches for applying image processing to diagnose and detect late blight. A suggested taxonomy for late blight detection has also been provided. In the same way, a model for the development of the solutions to problems is also presented. Finally, the research gaps have been presented in terms of open issues for the provision of future directions in image processing for the researchers.