Smart Farming Solutions: Automated Crop and Plantation Disease Detection

Abstract

The work presented in this Ph.D. thesis focuses on developing detection and recognition systems for diseases that affect agricultural crops and plantations based on deep learning and machine learning. The proposed detection and recognition systems in the literature offer precise solutions for early identification and effective management. However, they still face several challenges and they still limited in their uses. The first challenge is their lack of robustness and generalization. Their use is limited to the types of crops and diseases encountered during the learning process, leading to problems when faced with new types of crops and diseases not seen in the training phase. The second challenge is that the majority of these systems are designed to detect only one disease at a time and do not address the problem of simultaneous multi-disease detection. Another challenge is that most proposed systems detect diseases from leaves, which is common because leaves are often the first place where diseases appear in plants. However, some diseases infect the tree branches and do not appear on the leaves. To tackle these challenges, we have introduced three plant disease recognition systems. The first system is based on deep learning, capable of distinguishing between healthy and diseased leaves regardless of the crop type and disease, even if the system hasn't encountered them during the training phase. The primary idea is to prioritize the identification of diseased small leaf regions rather than solely relying on the overall appearance of the diseased leaf. Moreover, it includes assessing the disease's prevalence rate across the entire leaf. To ensure efficient classification, we employ a Small Inception model architecture, which is adept at processing small regions without compromising performance. The second proposed system is a deep learning-based model designed to detect and recognize multiple diseases simultaneously from any crop type, including those not encountered during the training process. Our method enables the independent recognition of each disease's symptoms within small leaf regions, regardless of the presence of other diseases on the same leaf and irrespective of the crop type. This is accomplished through the isolation method, which isolates each region containing specific disease symptoms and eliminates the influence of crop characteristics, in conjunction with the Small Inception model architecture. Additionally, our approach enables the calculation of the prevalence rate of each disease on the leaf and the determination of the overall extent of all diseases present on the leaf. The third system is a machine learning-based approach designed for detecting and segmenting diseases from tree branches, specifically targeting Nectria cinnabarina disease on apple tree branches. This system utilizes image processing techniques to enhance image quality and precision, thus facilitating the task for the Gaussian Mixture Model classifier. This classifier learns the probability distribution of the disease color and generates a segmentation mask, which is then utilized to identify the diseased areas on the branch. The results obtained from the experiments on the three systems demonstrate the effectiveness of the proposed methods in enhancing the accuracy of plant disease detection. Moreover, they outperformed existing methods by successfully identifying diseases across various crop types, detecting multiple diseases simultaneously from the same leaf, and accurately identifying and segmenting diseases from branches.