Plant Leaf Disease Detection Using CNN Algorithm

Deepalakshmi P., Prudhvi Krishna T., Siri Chandana S., Lavanya K. and Parvathaneni Naga Srinivasu
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Deepalakshmi P.: Kalasalingam Academy of Research and Education, India
Prudhvi Krishna T.: Kalasalingam Academy of Research and Education, India
Siri Chandana S.: Kalasalingam Academy of Research and Education, India
Lavanya K.: Kalasalingam Academy of Research and Education, India
Parvathaneni Naga Srinivasu: Anil Neerukonda Institute of Technology and Sciences, India

International Journal of Information System Modeling and Design (IJISMD), 2021, vol. 12, issue 1, 1-21

Abstract: Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detection and plant leaf disease detection are the noteworthy applications of precision agriculture. The main aim of this paper is to identify the diseased and healthy leaves of distinct plants by extracting features from input images using CNN algorithm. These features extracted help in identifying the most relevant class for images from the datasets. The authors have observed that the proposed system consumes an average time of 3.8 seconds for identifying the image class with more than 94.5% accuracy.

Date: 2021
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