Journal Article

Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform

Gajjar, Nagendra, Ruparelia, Stavan, Gajjar, Ruchi, Patel, Nikhilkumar Pareshbhai, Thakor, Vaibhavkumar Jigneshkumar

2022

Overview

Early identification of crop disease can aid the farmers to take timely precautions and countermeasures for its removal. In this paper, a real-time system to identify the type of disease present in a crop based on leaf images using machine learning is proposed. A deep convolutional neural network architecture is proposed to classify the crop disease, and a single shot detector is used for identification and localization of the leaf. These models are deployed on an embedded hardware, Nvidia Jetson TX1, for real-time in-field plant disease detection and identification. The disease classification accuracy achieved is around 96.88%, and the classification results are compared with existing convolutional neural network architectures. Also, the high success rate of the proposed system in the actual field test makes the proposed system a completely deployable system.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Accuracy / Agricultural production / Artificial Intelligence / Artificial neural networks / Cameras / Chlorophyll / Classification / Computer Graphics / Computer Science / Corn / Crop diseases / Datasets / Field tests / Identification / Image Processing and Computer Vision / Leaves / Legumes / Localization / Machine learning / Medical diagnosis / Neural networks / Original Article / Pathogens / Plant diseases / Real time / Rice