CCNet: CNN CapsNet-Based Hybrid Deep Learning Model for Diagnosing Plant Diseases Using Thermal Images

Plant disease diagnosis at an early stage enables farmers, gardeners and agricultural experts to manage and control the spread of illnesses in a timely and suitable manner. The traditional methods of plant disease diagnosis are expensive and might need significant manpower and advanced level machinery. In addition to that, conventional methods, such as visual inspections are prone to subjectivity, time constraints and error susceptibility. In comparison to that, computer based methods such as machine learning is accurately predicting plant diseases underscore the need for a transformative approach. However, by focusing solely on visualized contents and thermal images, these methods overlook the potential insights hidden within customer-posted images that may leads to low accuracy. This study is an attempt to addresses these gaps by proposing an alternative methodology which relies on a hybrid deep learning framework called CCNET. The core CCNET is the utilization of the superiorities of Convolutional Neural capsule network models to get better architecture for plant diseases diagnosis. The proposed CCNET effectively amalgamates the strengths of convolutional layers for spatial feature extraction and the sequential modelling capabilities of CNN and CapsNet for capturing temporal dependencies within image data. The performance of the CCNET has been evaluated through rigorous experimentation. The outcomes underscore the remarkable prowess of the proposed model with the accuracy of 94%. When it compared to the conventional methods, the CCNET surpasses all of them in terms of precision, recall, F-Score, and accuracy.