DESIGNING A SMART PLANT HEALTH DIAGNOSIS MODEL USING ADAPTIVE MACHINE LEARNING TECHNIQUES

Abstract

Plant health influences both agricultural productivity and food security worldwide because plant diseases threaten crop yields, together with economic stability and food quality. Traditional diagnostic methods demand manual inspection, which results in time-consuming processes that are costly and error-prone in resource-limited environments. This research presents a Smart Plant Health Diagnosis Model that utilizes adaptive machine learning techniques to address current limitations. This model provides accurate diagnostic solutions that scale effectively for plant health detection through image analysis, which supports early detection and sustainable agriculture.
The model utilizes Convolutional Neural Networks (CNNs) together with Vision Transformers to achieve highly precise plant disease classification. The model combines real-world images with synthetic data created by Generative Adversarial Networks (GANs) to improve its generalization ability across different crops and environmental conditions. The prototyping methodology ensures the model experiences continuous cycles of prototyping and validation. The model undergoes training and validation processes using publicly available datasets like PlantVillage as well as original data from agricultural field studies. The model evaluation involves precision, recall, accuracy, and F1-score measurements, and real-world tests determine its robustness across different environmental conditions.
The model reaches 96.3% accuracy during testing and sustains 93.2% accuracy in actual field conditions while surpassing current models in performance. The model's lightweight architecture, combined with attention mechanisms, facilitates deployment on low-cost devices, which enables farmers in resource-constrained regions to utilize this technology. The research connects adaptive technology with specific farming requirements to enhance precision agriculture methods. The model stands as a transformative solution for plant disease diagnosis, which provides farmers access to swift and dependable diagnostic instruments. The system facilitates early disease detection and accurate treatments to strengthen agricultural resilience while boosting crop production and contributing to worldwide food security.