Cocoa Pod disease detection based on convolutional neural network for healthy landscape Available to Purchase

The cocoa industry is a significant contributor to the global economy, with cocoa beans serving as a primary ingredient in various popular products such as chocolates, cocoa powder, and cocoa butter. However, cocoa farming is not without its challenges, as cocoa pod diseases can significantly impact crop yields and economic profits. Consequently, there is a developing need to develop efficient methods for identifying and categorizing cocoa bean pods either healthy or diseased. To address this issue, the authors of the paper implemented deep learning algorithms, specifically a dense convolutional neural network (CNN), to perform cocoa pod disease detection. The main goal of this analysis was to develop an effective method for identifying defects in cocoa pods. To train their deep learning model, the authors collected a dataset of Cocoa Pod images from Google and Kaggle datasets. By using a dense CNN model, the authors were able to accurately classify cocoa pods as healthy or defective. The proposed method is a promising approach for identifying diseased cocoa pods and can potentially be used to aid in the early detection and management of cocoa pod diseases, ultimately improving crop yields and economic profits for cocoa farmers.