Jul 2026· International Conference Computing Methodologies and Communication· pp. 1169-1174· 0 citations· 14 references
Abstract
Plant diseases are among the most significant challenges in agriculture because they reduce crop productivity and cause economic losses to farmers. Early monitoring of plant diseases is important for controlling infection spread and improving crop management. Traditional plant disease detection techniques depend heavily on manual inspection by agronomists; therefore, they are time-consuming, labor-intensive, subjective, and often inaccessible to farmers in remote locations. In recent years, plant disease classification has been performed using deep learning architectures such as CNN, ResNet, MobileNet, and EfficientNet. Although these models can be accurate, many of them require higher computational resources and usually focus only on disease detection without providing a practical treatment recommendation. This paper proposes a smart crop health monitoring system for plant leaf disease classification and pesticide advisory using the Ultralytics YOLO26s-cls model. The proposed system is trained on the New Plant Diseases Dataset containing 87,867 plant leaf images from 14 crop species and 38 disease/healthy classes. The pipeline includes image preprocessing, augmentation, YOLO26s-cls-based feature extraction, disease classification, and a validated rule-based pesticide recommendation module. The model architecture, model selection rationale, overfitting-control strategy, dataset validation procedure, and pesticide-rule validation process are explicitly described to support reproducibility. The proposed classifier achieved 99.02% validation accuracy, 99.01% macro precision, 99.00% macro recall, and 99.00% macro F1-score. The system can therefore serve as a decision-support tool for early disease identification and pesticide selection in precision agriculture.
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.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 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 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.
Various convolutional neural network architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks.
Allupati Chakradhar Patro· International Journal of Sci...· 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.
Agriculture continues to be one of the principal contributors to the economy and food security of developing nations, yet farmers regularly face difficulties such as unpredictable weather, variable soil conditions, and crop diseases that reduce quality and income. This paper presents a Crop Prediction and Plant Disease Detection System that integrates machine learning and deep learning techniques within a single web-based platform. The system accepts agricultural parameters — nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall — and applies a Random Forest regression model to estimate the expected crop. In parallel, it allows farmers to upload images of crop leaves, which are pre-processed and classified by a Convolutional Neural Network (CNN) to identify plant diseases and recommend suitable treatment. The application is built using ASP.NET Core for the user-facing interface, authentication, and dashboard, while a Python-based REST API hosts the machine learning and deep learning models; Microsoft SQL Server is used for persistent storage of user data, predictions, and disease records. The proposed system combines two traditionally separate functions — crop estimation and disease diagnosis — together with treatment recommendations and prediction history, into a single decision-support tool for precision agriculture, and was validated through unit, integration, and system-level testing
Preetham B, Madhura B, Dr. Manjunath B· International Journal of Adv...· 0 citations
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