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- Title
Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt.
- Authors
Veeramreddy, Rajasekhar; Gnanasekaran, Arulselvi; Babu, Kunchala Suresh
- Abstract
Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers' income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model's performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.
- Subjects
INDIA; CONVOLUTIONAL neural networks; RICE diseases &; pests; RICE farming; PLANT diseases; DEEP learning; CROPS; RICE
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2024, Vol 14, Issue 2, p1739
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v14i2.pp1739-1749