Hybrid Learning Approach for Identifying Plant Characteristics and Diseases Through Image Analysis

Advancements in deep learning and computer vision have enabled significant progress in the automated analysis of plant images, aiding in the detection of diseases and characterization of plant traits. Combining these technologies into hybrid models offers the potential for enhanced accuracy and efficiency in addressing the complexities of plant disease identification in agricultural practices. Proposed research presents the development of a system based on deep hybrid learning for analyzing plant images, identifying various characteristics, and detecting diseases using computer vision techniques. The Plant Village dataset, comprising a diverse collection of plant images, was utilized to train and test the proposed models. The goal was to enhance the efficiency and accuracy of plant disease detection while creating a refined dataset to improve model performance in subsequent iterations.Two hybrid models were proposed to achieve superior results- Hybrid (DenseNet + SVM) and Hybrid (DenseNet + XGB). In both cases, the DenseNet architecture was employed for feature extraction, leveraging its deep layers to capture intricate details from the plant images. The extracted features were then passed through Support Vector Machines (SVM) and Extreme Gradient Boosting (XGB) classifiers, respectively, to classify the plant characteristics and identify diseases. The rationale behind selecting SVM and XGB was to exploit their ability to handle complex decision boundaries and high-dimensional data efficiently.The proposed models were evaluated extensively, achieving an accuracy of over 96% in both cases. The Hybrid (DenseNet + SVM) model demonstrated robustness in handling complex variations in plant characteristics and diseases, while the Hybrid (DenseNet + XGB) model excelled in terms of precision and computational efficiency. These hybrid approaches outperformed traditional deep learning models, providing improved classification performance and greater generalizability. The study emphasizes the creation of a new dataset using the extracted features and classification results, enabling further refinements in training and enhancing the accuracy of future models. The proposed hybrid learning framework demonstrates the potential to revolutionize plant disease detection, making it a valuable tool for researchers and practitioners in precision agriculture.