Title: Deep learning model for plant-leaf disease detection in precision agriculture

Authors: Chandrabhanu Bajpai; Ramnarayan Sahu; K. Jairam Naik

Addresses: Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

Abstract: Crop disease in the agricultural is the main factor limiting yield and food quality. It requires timely diagnosis of crop illnesses for the better economy developing country. Manual crop illness assessment is limited due to lesser accuracy and restricted accessibility. It is very difficult to accurately identify and classify plant diseases due to corrupt in the data samples, lesser intensity of foreground and background, and the extreme similarity between unhealthy and healthy leaves in terms of colouring and size of crop leaves. Hence the employments of automated and computerised optimisations are needed. To identify plant leaf diseases, a DNSVM classification strategy fusing DenseNet-201 with support vector machine (SVM) is proposed in this work. Plant-Village dataset that provides good-variations, colour-differences, differences in orientation and size-of-leaves. Sugarcane plants-leaves were used for performance analysis of proposed model and obtained 97.78% of classification accuracy over the existing DenseNet-121-based classifier model (94%).

Keywords: plant leaves; disease detection; deep learning models; agriculture; classification; DenseNet-201.

DOI: 10.1504/IJISTA.2023.130562

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.1, pp.72 - 91

Received: 06 Jan 2023
Accepted: 27 Feb 2023
Published online: 27 Apr 2023 *