Enhancing Plant Health Monitoring in Precision Agriculture Through Image Segmentation and Ensemble Learning

The chapter delves into the critical importance of accurate and rapid plant disease detection in modern agriculture, emphasizing the limitations of traditional methods in the face of technological advancements. It introduces a cutting-edge approach that combines image segmentation and ensemble learning to enhance the precision and reliability of plant disease identification. The study employs advanced image segmentation techniques to isolate diseased leaves and remove irrelevant background information, thereby improving the model's ability to generalize to new data. Additionally, the chapter explores the use of ensemble learning methods, specifically majority voting, to combine the predictions of multiple convolutional neural network architectures, resulting in a highly accurate and robust classification model. The experimental results demonstrate the superior performance of the proposed approach compared to standard models such as VGG16, ResNet, MobileNet, and Vision Transformers. The chapter also discusses the practical implementation of the models, including data preprocessing, fine-tuning, and evaluation metrics, providing a comprehensive overview of the techniques and methodologies employed. Furthermore, the chapter highlights the potential applications of the proposed approach in real-world settings, including the deployment of the model on IoT devices and the development of an intelligent platform for real-time plant disease identification. The findings of this research have significant implications for the advancement of precision agriculture and the promotion of smart and sustainable farming practices.