Jul 2026· World Journal of Advanced Engineering Technology and Sciences· 0 citations
TL;DR
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Abstract
Plant leaf diseases are a major concern in agriculture because they reduce crop quality, lower productivity, and cause economic losses to farmers. Early detection of these diseases is important for protecting plants and improving overall crop management. However, manual identification through visual inspection is often slow and may produce inaccurate results, especially in the early stages of infection. To overcome this problem, this work proposes an automated leaf disease detection system using image processing and deep learning techniques. OpenCV is used for preprocessing the leaf images through resizing, noise removal, color normalization, and enhancement, while a Convolutional Neural Network (CNN) is trained to extract important features and classify the images into different disease categories. The dataset consists of healthy and diseased leaf images collected from publicly available sources and is divided into training, validation, and testing sets for effective model development. For practical implementation, the trained model is integrated into a Flask-based web application that allows users to upload leaf images and obtain predictions easily. The performance of the system is evaluated using accuracy, precision, recall, and F1-score, and the model achieved an accuracy of 95%. The results show that the proposed system can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper proposes an AI-based leaf disease detection system using deep learning and transfer learning, in which EfficientNetB5 serves as a pretrained feature extractor to classify 38 plant disease categories spanning 14 crop species. Preprocessing includes HSV-based leaf segmentation, resizing to 456×456 pixels, and EfficientNet-specific normalization. A compact two-layer dense classifier is trained on the 2,048-dimensional feature vectors produced by the frozen backbone. The system achieves an overall validation accuracy of 96.49%, macro-average precision of 0.97, recall of 0.96, and F1-score of 0.96 on 2,280 held-out images. Beyond classification, the system provides actionable cure and precautionary recommendations for every detected disease, making it directly useful to smallholder farmers. Comparative analysis with ResNet50, VGG16, and MobileNetV2 confirms that EfficientNetB5 achieves the highest accuracy with a favorable parameter-to-performance ratio. Multi-class ROC evaluation further demonstrates strong discriminative capability across all disease categories.
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations
The proposed Sugarcane Leaf Disease Detection and Classification System provides a fast, accurate, and user-friendly solution for automated disease diagnosis and contributes to improved crop management, reduced crop losses, and enhanced agricultural productivity.
The proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.
Sonali Kamra, Vijay Laxmi· International Journal of Res...· 0 citations
Plant leaf diseases are known to affect agricultural productivity and food security on a global level. "Therefore, the detection and diagnosis of diseases are important aspects of maintaining the health of crops on a sustainable level. Traditionally, the detection of diseases in plants is performed manually by experts. This process is considered to be a tedious and time-consuming task due to the chances of human error during the process. To overcome the challenges of traditional methods of detecting and diagnosing diseases in plants, a deep learning-based system is proposed in this research study to detect and classify diseases in plants. This system uses the object detection model YOLOv8x and YOLOv10x to detect the objects in the images and classify the images accordingly. Deep learning models are used to classify the images of the plants. This study uses various deep learning models like the convolutional neural network model ResNet50 and EfficientNet, and the transformer model Vision Transformer and Swin Transformer. Moreover, a hybrid model is proposed in this study by combining the transformer and convolutional neural network model to improve the efficiency of the system in detecting and classifying the diseases of the plants. This system uses the PlantVillage dataset to classify the images of the plants and detect the diseases accordingly. From the results obtained in this study, it can be observed that the proposed system is highly efficient in detecting and classifying the diseases of the plants with the help of the transformer and hybrid model.
India’s economy is primarily based on agriculture. Agriculture has significant contribution in nation’s GDP. Food security and employment significantly influenced by agriculture. However factors like uncertain weather conditions, poor quality of seeds and plant diseases impact on agriculture productivity. Computer vision and DL algorithms are most crucial components of precision agriculture. Early detection can improve decision making, maximize pesticide use, and preserve harvests. Using CNN architectures, segmentation-based approaches, handcrafted feature-based methods, and hybrid approaches incorporating Machine Learning and Deep Learning this study seek to provide review of recent publications from 2020 to 2026. The review was carried out using a variety of publications with different datasets, methodologies, and outcomes. The findings show that DL, especially CNN and transfer learning models, performed better than machine learning techniques. It points out several significant problems, such as dataset imbalance, insufficient generalization, computing inefficiency, and a dearth of real-world data. Future research topics are also suggested which includes IoT-driven real-time solutions, lightweight architecture, domain adaption, and multimodal imaging. This review aims to develop plant disease detection technologies that are more dependable, scalable, and field deployable.
Yuvraj Gholap, Rahul Joshi, Milind Gayakwad et al.· Journal of Intelligent Decis...· 0 citations
