Tomato leaf disease detection method based on improved YOLOv8n.

The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it's similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

In so far as everyone needs food, farming helps to provide for this basic requirement. Despite making up only 15.4% of the country's GDP, the agriculture sector is crucial to India since it creates a large number of job possibilities. In addition to providing useful information, the use of machine learning is improving farming by preventing or minimizing losses. Machine learning includes situations in which a machine may learn and make choices based on predetermined criteria. It may be classed as an area of artificial intelligence (AI). Artificial Neyral Networks (ANN), one of hottest artificial intelligence approaches of the present day, are extended by deep learning, a popular and contemporary data analysis tool in the last ten years. As the world's population rises, agriculture becomes more and more significant.This article discusses modern machine learning applications in agriculture and how they address issues before during, and after agricultural production.Numerous deep learning and machine learning methods, databases performances were reviewed in the publications. They grouped the approaches they looked at according to the most popular techniques, the kinds of data used, the performance metrics, and the results of these analyses.To detect tomato leaf diseases, machine learning techniques and image processing are used, albe it their accuracy is lower. Initially, tomato leaf diseases are automatically identified utilizing traditional image processing and machine learning techniques, yet the accuracy is lower.

Tomato plants are particularly vulnerable to leaf diseases, which can take a serious toll on crop yield and quality if not caught early. Traditionally, farmers and agricultural experts rely on manual inspection to spot these diseases a method that can be both slow and prone to mistakes. To streamline this process, our project introduces an automated system that uses machine learning and image processing to detect tomato leaf diseases more accurately and efficiently. We worked with a dataset of tomato leaf images that includes both healthy leaves and those affected by diseases like Early Blight, Late Blight, Leaf Mold, and Target Spot. Using this data, we trained a Convolutional Neural Network (CNN) to classify the images with high accuracy. To improve the model’s learning, we applied standard preprocessing steps such as image augmentation, resizing, and normalization. Our experiments show that the system performs well, offering reliable early detection of various tomato leaf diseases. By providing timely insights, this tool can help farmers take preventive action sooner, potentially reducing crop loss and improving productivity. In the future, we hope to expand this work into mobile platforms to make real- time disease detection more accessible in the field. Key Words: Tomato leaf diseases, disease detection, deep learning, CNN, image processing, Late blight, Leaf mold.

Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co‐occurrence matrix (GLCM) and scale‐invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state‐of‐the‐art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

This study explores the transformative potential of integrating Artificial Intelligence (AI) with precision agriculture to address key challenges in farming, such as disease detection, resource optimization, and yield improvement. By leveraging the Real-Time Detection Transformer (RT-DETR) framework, the study combines high-resolution imaging, hyperspectral scanners, and real-time data processing to enable efficient and accurate detection of tomato leaf diseases. A robotic system, equipped with autonomous navigation and non-invasive diagnostic tools, was developed to classify diseases and provide actionable insights for sustainable crop management. The study evaluates the performance of object detection models, such as YOLOv8 and RT-DETR, on the PlantVillage and PlantDoc datasets. RT-DETR achieving a mean Average Precision (mAP) of 0.988 on the controlled PlantVillage dataset and 0.402 on the complex PlantDoc dataset. By integrating AI with farming practices, this research highlights a pathway to improving agricultural productivity, environmental resilience, and global food security.

Tomato leaf diseases pose a significant threat to global food security, necessitating accurate and efficient detection methods. This paper introduces the Tomato Leaf Disease Visual Language Model (TLDVLM), a novel approach based on the BLIP-2 architecture enhanced with Low-Rank Adaptation (LoRA), for precise classification of 10 distinct tomato leaf diseases. Our methodology integrates a sophisticated image preprocessing pipeline, utilizing GroundingDINO for robust leaf detection and SAM-2 for pixel-level segmentation, ensuring that the model focuses solely on relevant plant tissue. The TLDVLM leverages the powerful multimodal understanding of BLIP-2, with LoRA applied to its Q-Former module, enabling parameter-efficient fine-tuning without compromising performance. Comparative experiments demonstrate that the TLDVLM significantly outperforms baseline models, including CLIP-LoRA and ConvNeXT-tiny, achieving an accuracy of 97.27%, a precision of 0.9587, a recall of 0.9789, and an F1-score of 0.9681. Beyond classification, the finetuned TLDVLM checkpoints are integrated into a practical application for new image inference. This application displays the raw and segmented images, the predicted disease, and offers functionalities to fetch comprehensive information on disease causes and remedies using external APIs (e.g., OpenAI), with an option to download a PDF summary for offline access on a portable device. This research highlights the potential of LoRA-adapted Vision-Language Models in developing highly accurate, efficient, and user-friendly agricultural diagnostic tools.

Rice leaf diseases pose a significant and escalating threat to global food security. Timely and accurate detection, particularly in the critical early stages characterized by subtle lesions, is paramount for effective disease management. However, existing solutions often struggle with the complexities of real-world field environments (e.g., variable lighting, occlusions, complex backgrounds), computational constraints on edge devices, and limited generalizability across diverse disease types and plant species. To address these challenges, this study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection. Our key innovations include: (1) A Multi-branch Large-kernel Fusion Depthwise (MLFD) module enhancing multi-scale contextual feature extraction critical for identifying subtle early lesions; (2) A Multi-scale Dilated Transformer Attention (MDTA) module integrating spatial and channel attention mechanisms to improve feature representation under complex conditions; (3) A Lightweight Detection Head (Lo-Head) optimized with grouped and depthwise convolutions, drastically reducing model complexity without sacrificing accuracy. Crucially, extensive experiments demonstrate the framework's superior performance. On a dedicated rice leaf disease dataset, it achieves a mean Average Precision mAP@0.5:0.95 of 62.62%, outperforming state-of-the-art lightweight detectors including YOLOv5n (56.73%), YOLOv8n (57.41%), YOLOv10n (56.14%), and the baseline YOLOv11n (60.85%), while maintaining low computational demands (6.3 GFLOPs, 2.66M parameters). Significantly, rigorous generalization experiments validate the model's exceptional transferability. Evaluated on independent datasets encompassing potato and tomato leaf diseases, the proposed framework consistently surpasses comparable models in mAP@0.5:0.95, demonstrating its robust capability to detect diseases across different plant species. This combination of high accuracy, computational efficiency, and remarkable cross-crop generalizability positions our framework as a highly promising tool for practical deployment on resource-limited edge devices (e.g., drones, field sensors) in smart agriculture systems, enabling proactive disease surveillance and precision control strategies across diverse crops.

The early and accurate detection of diseases in tomato plants is crucial for ensuring high crop yields and quality. Traditional methods of disease identification are often labor-intensive, time-consuming, and subject to human error. This research presents an enhanced classification system for detecting tomato leaf diseases using Convolutional Neural Networks (CNNs). Leveraging the power of deep learning, our approach significantly improves the accuracy and efficiency of disease identification compared to conventional techniques. We developed a CNN-based model that was trained and validated using a comprehensive dataset of tomato leaf images, encompassing various disease classes and healthy samples. The model architecture was meticulously designed to optimize feature extraction and classification performance. Extensive data augmentation techniques were employed to enhance the robustness and generalization capability of the model. Our enhanced classification system achieved remarkable accuracy, outperforming existing models in the literature. The results demonstrate the model's ability to effectively differentiate between multiple disease types and healthy leaves, even under challenging conditions such as varying lighting and background noise. This advancement holds great potential for integration into automated agricultural systems, providing farmers with a reliable tool for early disease detection and management.The implementation of this CNN-based improved classification system can lead to significant advancements in precision agriculture, minimizing crop losses and promoting sustainable farming practices. Future work will focus on extending this approach to other crops and refining the system for real-time field applications.

Tomato leaf diseases pose a severe danger to agricultural productivity and global food security. The automated tomato leaf disease detection system presented in this study is based on a hybrid deep learning model that combines the feature extraction capabilities of the VGG16 and ResNet50 architectures. The system was tested on a publicly available dataset of tomato leaf images against 4 baseline models: CNN, ResNet50, VGG16, and MobileNetV2. With a 97.99% accuracy, a 98.04% precision score, 97.99% recall score, and a 97.98% F1-Score, the suggested hybrid model performs better than individual models. This was verified after extensive trials with the datasets. The model's resilience and efficacy in reliably diagnosing a variety of tomato leaf diseases were further validated by visual studies such as confusion matrices, bar graphs, radar charts, and heatmaps. Since the model can capture both fine-grained textures and deep hierarchical characteristics, the hybrid model performs better, providing more thorough pattern identification. Future research should concentrate on improving the system for edge devices, extending to multi-crop disease detection, and integrating explainable AI approaches. The suggested approach presents a viable first step toward automated, scalable, and accurate plant disease monitoring, which will enhance crop management, lower the need for pesticides, and promote sustainable farming methods.

Indian economy is mostly dependent on agriculture. One of the highly used food crops in India is Tomato. Hence detection and analysis of leaf disease on tomato plants so as to increase the yield is highly essential. It becomes very hard to manually detect and analyze the tomato leaf diseases. Hence, in this paper we have proposed a segmentation-based approach for automatic segmentation of infected regions. The segmented area is further analyzed for disease classification and severity measurement. Leaf disease detection technique proposed here involves various stages including preprocessing, segmentation, feature extraction, training and classification followed by the severity measurement from the disease segmented region. We have analyzed the performance of different features extraction techniques including color, texture and shape features along with various classification techniques. The performance of the proposed system really inspires the farmers to use the automated system for detection and severity measurement of tomato plant disease.

Tomato leaf diseases significantly affect crop productivity, and manual inspection often leads to misclassification due to the visual similarity of symptoms. Recent studies have shown that Convolutional Neural Networks (CNN) provide high accuracy in leaf–based plant disease classification across various plant species, highlighting their potential for early disease detection. This study aims to develop an accurate tomato leaf disease classification system using a CNN model trained on the Kaggle tomato leaf dataset consisting of four classes: Leaf Blight, Bacterial Spot, Leaf Scab, and Healthy. The methodology includes literature review, dataset acquisition, preprocessing, augmentation, CNN architecture design, model training, and performance evaluation. Preprocessing techniques such as resizing and normalization were applied, followed by augmentation using random flipping and rotation to increase dataset variability. The proposed model was trained for 40 epochs with a batch size of 16. Results show consistent accuracy improvement, reaching 0.98 training accuracy with a loss of 0.07, while validation accuracy peaked at 0.94. Testing on both single and multiple images demonstrates strong prediction confidence, with minor misclassifications in visually similar cases. Overall, the system effectively identifies tomato leaf diseases and reinforces the suitability of CNN for supporting early plant disease detection in smart agriculture applications.

Remarkable inter-class similarity and intra-class variability of tomato leaf diseases seriously affect the accuracy of identification models. A novel tomato leaf disease identification model, DWTFormer, based on frequency-spatial feature fusion, was proposed to address this issue. Firstly, a Bneck-DSM module was designed to extract shallow features, laying the groundwork for deep feature extraction. Then, a dual-branch feature mapping network (DFMM) was proposed to extract multi-scale disease features from frequency and spatial domain information. In the frequency branch, a 2D discrete wavelet transform feature decomposition module effectively captured the rich frequency information in the disease image, compensating for spatial domain information. In the spatial branch, a multi-scale convolution and PVT (Pyramid Vision Transformer)-based module was developed to extract the global and local spatial features, enabling comprehensive spatial representation. Finally, a dual-domain features fusion model based on dynamic cross-attention was proposed to fuse the frequency-spatial features. Experimental results on the tomato leaf disease dataset demonstrated that DWTFormer achieved 99.28% identification accuracy, outperforming most existing mainstream models. Furthermore, 96.18% and 99.89% identification accuracies have been obtained on the AI Challenger 2018 and PlantVillage datasets. In-field experiments demonstrated that DWTFormer achieved an identification accuracy of 97.22% and an average inference time of 0.028 seconds in real plant environments. This work has effectively reduced the impact of inter-class similarity and intra-class variability on tomato leaf disease identification. It provides a scalable model reference for fast and accurate disease identification.

Automated detection of apple leaf diseases is crucial for predicting and preventing losses and for enhancing apple yields. However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. To address the challenges of detecting small-target diseases on apple leaves in complex backgrounds and difficulty in mobile deployment, we propose an enhanced lightweight model, ELM-YOLOv8n.To mitigate the high consumption of computational resources in real-time deployment of existing models, we integrate the Fasternet Block into the C2f of the backbone network and neck network, effectively reducing the parameter count and the computational load of the model. In order to enhance the network’s anti-interference ability in complex backgrounds and its capacity to differentiate between similar diseases, we incorporate an Efficient Multi-Scale Attention (EMA) within the deep structure of the network for in-depth feature extraction. Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. Finally, we employ the NWD loss function to replace the CIoU loss function, allowing the model to locate and identify small targets more accurately and further enhance its robustness, thereby facilitating rapid and precise identification of apple leaf diseases. Experimental results demonstrate ELM-YOLOv8n’s effectiveness, achieving 94.0% of F1 value and 96.7% of mAP50 value—a significant improvement over YOLOv8n. Furthermore, the parameter count and computational load are reduced by 44.8% and 39.5%, respectively. The ELM-YOLOv8n model is better suited for deployment on mobile devices while maintaining high accuracy.