Detection of Crop Diseases Using Enhanced Variability Imagery Data and Convolutional Neural Networks
Document Type
Article
Journal/Book Title/Conference
Computers and Electronics in Agriculture
Volume
193
Issue
106732
Publisher
Elsevier BV
Publication Date
1-21-2022
First Page
1
Last Page
8
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Abstract
The timely detection of crop diseases is critical for securing crop productivity, lowering production costs, and minimizing agrochemical use. This study presents a crop disease identification method that is based on Convolutional Neural Networks (CNN) trained on images taken with consumer-grade cameras. Specifically, this study addresses the early detection of wheat yellow rust, stem rust, powdery mildew, potato late blight, and wild barley net blotch. To facilitate this, pictures were taken in situ without modifying the scene, the background, or controlling the illumination. Each image was then split into several patches, thus retaining the original spatial resolution of the image while allowing for data variability. The resulting dataset was highly diverse since the disease manifestation, imaging geometry, and illumination varied from patch to patch. This diverse dataset was used to train various CNN architectures to find the best match. The resulting classification accuracy was 95.4 ± 0.4%. These promising results lay the groundwork for autonomous early detection of plant diseases. Guidelines for implementing this approach in realistic conditions are also discussed.
Recommended Citation
S. Kendler, R. Aharoni,S. Young, H. Sela, T. Kis-Papo, T. Fachima, and B. Fishbain, “Detection of crop diseases using enhanced variability imagery data and convolutional neural networks, ”Computers and Electronics in Agriculture, vol. 193, no. 106732, 2022.doi:10.1016/j.compag.2022.106732.
