1School of Equipment Engineering, Jiangsu Urban and Rural Construction Vocational College, Changzhou, 213000, China
2Department of Animation, Sangmyung University, 37, Hongjimun 2-gil, Jongno-gu, Seoul, Republic of Korea
3New Emerging Technologies and 5G Network and Beyond Research Chair, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
*Corresponding Author: Ok Hue Cho, Department of Animation, Sangmyung University, 37, Hongjimun 2-gil, Jongno-gu, Seoul, Republic of Korea, Email: profcho@smu.ac.kr
Online Published on 10 February, 2026.
Food security being one of the prominent global issues requires strategies to maximize plant productivity with due consideration to sustainability. In this regard, precision agricultural practices are incorporated and are quite promising. The use of technology i.e. integrating AI is rapidly changing the complete agricultural scenario. A quick identification of wilting ensures farmers take early action and remedies. An early detection of plant wilting in a real-time scenario can avoid huge food crop losses but the whole task is humongous. The images collected from open fields over large areas can be analyzed via various image processing techniques. Using the CNN model for identification of early images for quick response to plant care saves the time and effort of the farmers. CNN utilization allows prompt wilting detection and early corrective action to protect the crop. In this paper, CNN trained on image data allows high predictability of wilting detection at early stages in Soybean plants.
A well-defined dataset of 6704 pictures of soybean plants from agricultural fields is considered and allocated appropriately to train, test and validate the CNN model. Using image preprocessing, resizing and rescaling of images is done to ensure consistency of image dimensions. Noise elimination from the images is done via a low-pass filtering method to preserve low-frequency information and the images are converted to grayscale. Using standard Python libraries, data augmentation is ensured and 9158 images across all classes are arranged for the model. The performance evaluation matrix indicating accuracy percentage, precision percentage and recall percentage is estimated.
The proposed CNN algorithm is first calibrated using a set of images from the dataset and then tested for an entirely different set of images not used earlier. The overall accuracy is 91%. The model promises unambiguous identification of wilting in soybean leaves by appropriately classifying images set in 5 orders using a substantially ample dataset. Early identification in real time can prove to be of utmost benefit to the agricultural community in terms of eradication of the causes and yield retention of the crop.
Convolution neural networks (CNN), Machine learning, Plant wilting detection, Soybean wilting
