Jul 2026· International Conference Computing Methodologies and Communication· pp. 1254-1259· 0 citations· 11 references
Abstract
The general health condition of the crop is very essential for increasing the agricultural production and global food security. Fungal diseases in leaves may spread rapidly and result in yield reduction if not detected timely. To this end, an intelligent system is constructed based on deep convolutional neural network to classify the leaf diseases of plants using images. The CNN model can be further qualified by a diverse dataset coupled with preprocess and data augmentation for higher generalization capability. The CNN architecture drawn learns highly packed visual feature from the plant leaf image and can classify several types of disease with very high accuracy. Our experiment results verified the efficacy of the package with 92.23% classification accuracy which outperformed traditional image processing combined with classical machine learning approaches in the past. It can effectively be used for actual farming in the field with positive contribution to sustainable farming practices.
Performance evaluation using accuracy, model size, time per image, and number of parameters showed that the proposed model achieved high accuracy and provided better discrimination between visually similar disease classes.
An extensive set of experiments was conducted to evaluate the performance of the proposed model for plant disease detection, and it is demonstrated that the model achieves highly reliable results, with an accuracy of 97.13%.
Hayat Meddeber, M. Meddeber· ITEGAM- Journal of Engineeri...· 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
Plant diseases are one of the many factors which reduce agricultural productivity and global food security. Accurate and early diagnosis of plant diseases helps to reduce significant losses to crops and aid in sustainable agriculture. In recent years, deep learning methods for plant disease diagnosis have been of great interest in the field of agriculture. This study proposes a hybrid model, Hybrid plant disease classification using deep learning models, which uses a Convolutional Neural Network (CNN) to obtain local features of the affected plant leaves and a pre-trained Vision Transformer (ViT) to get global features. In this work, the proposed hybrid model is validated using 15 plant disease classes of which different data augmentation techniques such as changing the light exposure and object orientation are employed. Experimental results revealed the convergence stability of the model, the strong generalization ability, and the better accuracy as compared with each of the models used independently.
Vallem Ranadheer Reddy· Dandao Xuebao/Journal of Bal...· 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
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
