Development of AI-Driven Image Analysis for Plant Disease Identification

Thesis Abstract

The effective management of plant diseases remains a critical challenge in agricultural productivity, with traditional identification methods often reliant on manual visual inspection, which is labor-intensive, subjective, and susceptible to error. This study aims to develop a robust, AI-driven image analysis system to facilitate rapid, accurate detection and classification of plant diseases, thereby enhancing disease management strategies and crop yield optimization. Specific objectives include designing a convolutional neural network (CNN)-based model for image classification, compiling and annotating a comprehensive dataset of plant images representing multiple disease categories, evaluating the model’s performance against conventional diagnosis methods, and providing a framework for real-time disease detection using mobile devices. A mixed-methods research design was employed, incorporating both qualitative and quantitative components. The qualitative phase involved collecting expert knowledge through interviews with plant pathologists to identify key morphological features associated with various diseases, which informed the feature extraction process. Quantitatively, the study utilized a dataset of 10,000 high-resolution images sourced from agricultural research stations, local farms, and open-access repositories, representing ten commonly affected crops including cassava, maize, and tomato. The images were annotated using a custom-developed labeling tool by a team of agronomy specialists. The dataset was split into training (70%), validation (15%), and testing (15%) subsets. The core methodology involved training a CNN model, specifically leveraging transfer learning with a pre-trained ResNet-50 architecture, to classify plant disease images. The model’s hyperparameters were optimized through grid search, and its performance was evaluated using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Additionally, model interpretability was enhanced via Grad-CAM visualization to identify salient image regions influencing classification decisions. Data analysis involved computing classification metrics and conducting statistical significance testing through paired t-tests comparing the AI model’s diagnostic accuracy to that of human experts. The study further employed qualitative thematic analysis of expert interviews to understand practical deployment considerations and potential challenges faced in real-world application. The expected key findings include achieving an overall classification accuracy exceeding 92%, with sensitivity and specificity rates surpassing 90%, demonstrating comparable or superior performance to experienced plant pathologists. The results are anticipated to validate the efficacy of deep learning techniques in plant disease diagnostics and highlight areas for further improvement such as expanding the dataset to include multiple disease stages and environmental conditions. The contribution to knowledge chiefly lies in innovatively integrating AI-driven image analysis with plant pathology and demonstrating its practical applicability for real-time disease detection in diverse agricultural settings. This research advances existing literature by providing a scalable, user-friendly diagnostic framework capable of supporting farmers and agronomists with limited expertise, thereby reducing dependence on specialized laboratory testing and easing the burden of disease management. Moreover, it offers a theoretical foundation based on the synergistic application of convolutional neural networks and domain-specific botanical features, grounded within the framework of the Theory of Visual Perception and the Diffusion of Innovations theory. The study concludes that AI-based image recognition systems present a viable solution for early, accurate plant disease detection, with significant implications for sustainable agriculture and food security. Recommendations include further refinement of the model through transfer learning with larger, multi-temporal datasets; integration with mobile-based platforms for field deployment; and extensive validation across different agro-ecological zones. Future research should explore the incorporation of multispectral imaging and machine learning ensemble techniques to improve diagnostic precision and robustness, as well as consider socio-economic factors influencing adoption among smallholder farmers.