การประยุกต์ใช้โครงข่ายประสาทเทียมแบบคอนโวลูชันสำหรับการจำแนกภาพโรคใบอ้อยและการบูรณาการกับไลน์แชตบอตเพื่อสนับสนุนเกษตรอัจฉริยะ

Supachai Baipod; Charuay Savithi; Jackaphan Sriwongsa

doi:10.14456/jcct.2025.24

Authors

Supachai Baipod Student, Department of Smart City Management and Digital Innovation, Faculty of Mahasarakham Business School, Mahasarakham University, Thailand https://orcid.org/0009-0009-2032-2393
Charuay Savithi Associate Professor, Dr., Department of Smart City Management and Digital Innovation, Faculty of Mahasarakham Business School, Mahasarakham University, Thailand
Jackaphan Sriwongsa Lecturer, Program in Computer Science, Faculty of Science and Technology, Rajabhat Mahasarakham University, Mahasarakham 44000, Thailand https://orcid.org/0000-0002-1187-1130

DOI:

https://doi.org/10.14456/jcct.2025.24

Keywords:

Convolutional Neural Networks, Leaf Disease Diagnosis, Image Classification, Line Chatbot, Smart Agriculture

Abstract

Plant leaf diseases, particularly sugarcane leaf diseases in Thailand, represent a significant problem affecting sugarcane yield and quality. Traditional disease detection methods are often time-consuming and require specialized expertise. Therefore, this research aims to develop a deep learning model for sugarcane leaf disease diagnosis by applying Convolutional Neural Networks (CNN) techniques and integrating them with LINE chatbot to support smart agriculture. This study compared the performance of five CNN architectures to identify the optimal model for sugarcane leaf disease classification, including VGGNet-16, ResNet-50, DenseNet-121, AlexNet, and GoogLeNet, using the Sugarcane Leaf Disease Dataset collected by the researchers from real-world environments. The sugarcane leaf image dataset was collected from four provinces in the central northeastern region, totaling 4,000 images divided into four categories of 1,000 images each, covering four types of sugarcane leaves: red stripe disease, ring spot disease, rust disease, and healthy sugarcane leaves. The data was split into three ratios of 75:25, 80:20, and 90:10 for training and testing sets, respectively. Experimental results showed that the DenseNet-121 model with a 90:10 ratio achieved the highest performance, with a Macro-average of 97.28%. When the best model was developed into a LINE Chatbot system for automated sugarcane leaf disease diagnosis, system testing demonstrated effective user response capabilities. This research demonstrates the potential of deep learning technology to support farmers in quickly and accurately monitoring sugarcane plant health, which is crucial for developing smart agriculture systems in Thailand.

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