Jul 2026· SPU - Journal of Science, Technology and Management Research· pp. 44-60· 0 citations
TL;DR
A hybrid approach integrating XGBoost for feature selection and Support Vector Machine for classification is proposed to capture both overarching trends and intricate details, offering a robust, scalable, and accurate solution for early crop disease detection in precision agriculture.
Abstract
Crop disease identification is still a big problem in agriculture, which results in large yield losses and food lacks, especially in regions dependent on manual monitoring. Traditional methods of identifying plant diseases are often labor intensive, error-prone, and ineffective in early-stage diagnosis. To overcome these limitations, this study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data. The methodology utilizes the PlantVillage dataset, encompassing over 50,000 annotated leaf images across 14 crops. After rigorous preprocessing involving image resizing, normalization, and cleaning, Improved Deep Joint Segmentation is applied to localize disease-affected regions. Feature extraction incorporates color, texture (GLCM, LBP), and shape attributes to enhance classification accuracy. A hybrid approach integrating XGBoost for feature selection and Support Vector Machine (SVM) for classification is proposed to capture both overarching trends and intricate details. Experimental results across four major crops—potato, tomato, corn, and grape—demonstrate superior performance, with the hybrid model achieving 98.6% accuracy, 98.3% precision, 99.0% recall, and 99.1% F1-score. The model outperforms existing approaches, offering a robust, scalable, and accurate solution for early crop disease detection 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, A. Jajur. J· World Journal of Advanced En...· 0 citations
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
Agriculture is a key sector in developing economies, but crop diseases significantly impact productivity, food security, and farmers’ livelihoods. Early detection is crucial to minimize losses, yet traditional methods are slow, error-prone, and depend heavily on human expertise. Recent advancements in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled more efficient automated crop disease detection. This study reviews pre-2018 AI-based approaches, focusing on techniques such as image processing, feature extraction, and classification methods. It highlights models like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid systems combining traditional and modern techniques. The proposed approach includes preprocessing, segmentation, color transformation, and extraction of texture, color, and shape features, followed by supervised learning for classification. AI systems can detect subtle disease symptoms early, achieving over 90% accuracy under controlled conditions. Integration with mobile and IoT technologies enables real-time monitoring and decision support for farmers. However, challenges such as limited datasets, environmental variability, and computational constraints remain. Future work should focus on developing scalable, robust, and field-deployable solutions for diverse agricultural conditions.
Josh Rogers, Brandon Truaxe· International Journal of Mod...· 0 citations
Plant leaf diseases are a major cause of crop loss and create a serious problem for farmers. Traditional methods and earlier machine learning algorithms are not accurate enough and do not work well in real-time. To overcome these problems, this study develops a hybrid model integrating a neural network–based method with traditional predictive algorithms for accurate plant leaf disease detection. This work considers images from six types of plants apple, corn, grapes, peach, potato and strawberry. The model uses EfficientNetV2S to extract features, and Principal Component Analysis is used to reduce data size, which decreases computational cost and improves model performance. The extracted features are then evaluated using several predictive models such as Support Vector Machine, Random Forest, and XGBoost, where SVM provides strong classification in high-dimensional space, Random Forest improves robustness by reducing overfitting, and XGBoost enhances performance through gradient boosting. Additionally, a stacking ensemble method is used where predictions from SVM, Random Forest, XGBoost and EfficientNetV2S are fused using a meta-learning approach to improve overall predictive accuracy. The proposed framework has been tested on 17,459 images across 19 different plant disease classes. The model gives 94.01% accuracy with consistent results, so it can be used in real time. It also helps farmers make better decisions for plant management.
Chalapati Sowmya, Muppalla Bhavya Sri, P. Upadhyay· International Conference Com...· 0 citations
A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.
A. Elkholy, N. Elshennawy, Ahmed M. Gab Allah· Journal of King Saud Univers...· 0 citations
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.