Jul 2026· International Journal of Science and Research (IJSR)· 0 citations· 29 references
TL;DR
Understanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey and provides a comparative and performance analysis of these models by examining their techniques, merits, demerits, datasets used, and evaluation metrics.
Abstract
: Plant leaf diseases cause severe losses in crop yields and qualities, and account for considerable volume of losses to the agricultural output globally. Recognition of plant disease early and rightly is crucial to disease treatment and to reduce loss to the crop and to maintain agricultural sustainability. Plant disease that occurs on the leaves has been traditionally detected by farmers and experts with naked eyes by checking its symptoms like discoloration, spots and lesions. However, the process requires time, labour, expertise and is subjective, which renders it unusable for large-scale implemented agriculture. Recent years have seen the promising use of Artificial Intelligence (AI) as a tool for automated plant disease identification. The extraction of manually-crafted features from photographs of plant leaves, such as colour, texture, and form, is at the heart of many Machine Learning (ML) approaches used for disease classification. While these ML models have shown acceptable performance, they require significant manual feature engineering and can be poor at operating in real-world settings and with voluminous data. To address these issues, Deep Learning (DL) algorithms have found extensive usage in the identification and categorisation of plant leaf diseases. The You Only Look Once (YOLO) family of detection of objects models is making waves in the DL object detection space thanks to its impressive dual-tasking capabilities: object identification and multiple illness categorisation in a single pass, all at lightning speed and with pinpoint accuracy. For real-time disease identification in precision agriculture, YOLO stands out as an end-to-end feature learning and object recognition method, set apart from typical ML approaches. Understanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey. It also provides a comparative and performance analysis of these models by examining their techniques, merits, demerits, datasets used, and evaluation metrics.
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
Plant varieties are essential for the survival of human beings and animals, as they act as an alternative source of food,
fiber, fodder, and other raw materials for domestic needs and industries in any society. Early identification of plant
leaf diseases is very important for keeping the health of crops intact. Crops being infected can affect the overall yield
of crops, which may be detrimental to the earnings of farmers. With the emergence of artificial intelligence
technology, it has become possible to deploy systems for quicker identification of illnesses. This work has been
carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves.
For this purpose, the dataset has been created after retrieving data from the PlantVillage dataset. The clinical
reliability of different deep learning models of various representational capacities has been tested while using
ImageNet pre-trained parameters. The experimental results show that MobileNetV2 achieves the highest accuracy of
96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%). The test accuracy and class-wise F1-scores
for the CNN are observed to be substantially very high. The generalization ability and result of DCNN and MCNN are
moderate and poor, respectively, as observed. Additionally, the proposed CNN only uses the disease-affected areas on
the leaf, thus making the result more interpretable
Divya Singhal, Ankit Verma, Amit Kumar Gupta et al.· International Journal of Dru...· 0 citations
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
Agricultural production is exposed to losses when plants became infected with diseases from the environment, which in turn threatens food security; therefore, having the ability to detect plant diseases early on gives farmers the opportunity to minimize the loss they incur. Sunflower (Helianthus annuus) is Ranking among other oilseed crops worldwide for total agricultural production, and it has a high risk of being affected by diseases that cause reduced crop yield and quality, e.g., Downy mildew, grey mould, and leaf scars. Disease detection using conventional methods usually involves visual inspections by trained personnel; this process, however, is very time consuming, subjective, and could produce numerous errors. To counteract these issues, this paper proposes a hybrid learning framework for plant disease detection that combines deep feature-driven image analysis with a combination of two classifiers used in an ensemble mode to create a single final classification. The use of MobileNetV2 will act as the feature extraction process for the hybrid learning framework, while the classifier portion of the hybrid learning framework will consist of two K-Nearest Neighbour classifiers. Classification using SVM and RF classifiers will be completed using a combination of ensemble voting. Grad-CAM (gradient-weighted class activation maps) will be used to improve the interpretability of the disease classification results by identifying affected areas on images. Test results for the hybrid learning framework reveal an overall accuracy of 98.8%, compared to the accuracy of the two classifiers used separately: 95.8% for CNN, 93% for SVM, and 96.0% for RF. The confusion matrix for the two classifiers shows a significant number of accurate classifications with minimal misclassifications. The proposed hybrid learning framework for plant disease detection provides an effective method for detecting plant diseases in real time, interpretable results, and potential for scalability.
Kshirsagar Soumya, Mr. G Sekhar Reddy, D. G. L. A. Babu et al.· International Conference Com...· 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.
Plant diseases have long been considered a major threat to global food production systems. Therefore, early diagnosis is vital to mitigate the risk of these diseases. This task can be challenging, as the number of harmful diseases is substantial. One technology that has gained widespread interest is artificial intelligence, specifically deep learning, which is used to identify plant diseases using leaf patterns. This paper presents two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture. Experiments were carried out using the PlantCity dataset, which consists of twelve subsets of diverse crop species representing fruits, vegetables, and grains with variations among the subsets, including the number of classes, the subset sizes, class distribution, and visual complexity of disease symptoms. The two models were evaluated using multiple metrics, including accuracy, loss, precision, recall, and F1-score. Explainable AI using the LIME technique was deployed to better interpret the acquired results. Results showed that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model, with accuracies ranging from 86% to 99% across eleven experimented crops. In order to perform an independent experimental validation for the developed model, future work will focus on constructing a local crop dataset captured from Iraqi fields to evaluate the developed models based on the local environment.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations