1Department of Mathematics and Statistics, Chonnam National University, Republic of Korea
2Division of Culture Contents, Chonnam National University, Republic of Korea
*Corresponding Author: In Seop Na, Division of Culture Contents, Chonnam National University, Republic of Korea, Email: ypencil@hanmail.net
Online published on 21 November, 2024.
This paper employs deep learning in the classification of soybean wilting, a plant health indicator affected by external pressures, using a Convolutional Neural Network (CNN) with a pre-trained model. It highlights the promise of deep learning in agriculture by examining the relevance of wilting, evolution in the agricultural sector and applications in crop wellness monitoring.
A CNN is used in the study to classify soybean withering, with special attention to the VGG16 pre-trained model. Deep learning's ability to interpret complex data patterns is harnessed for intelligent and accurate wilting detection. A smart detection system tailored for soybean wilting is developed, incorporating recent advancements and addressing associated challenges.
The CNN model, notably VGG16, achieves 76% overall accuracy in distinguishing healthy and wilted soybean leaves, signifying a transformative shift in soybean crop health management. The approach offers a precise, efficient and sustainable solution supported by state-of-the-art CNN technology, advancing soybean cultivation practices.
Accuracy, Convolutional neural network (CNN), Soybean wilting, VGG16 pre-trained model
