Deep Learning-Based Computational Model for Disease Identification in Cocoa Pods (Theobroma cacao L.)
This work addresses disease identification for cocoa farmers, particularly younger or inexperienced ones, by providing a mobile app, but it is incremental as it applies an existing method to a new dataset.
The authors tackled the problem of early disease identification in cocoa pods by developing a deep learning model, achieving considerable accuracy in detecting and classifying diseases like 'monilia' and 'black pod' through image analysis.
The early identification of diseases in cocoa pods is an important task to guarantee the production of high-quality cocoa. The use of artificial intelligence techniques such as machine learning, computer vision and deep learning are promising solutions to help identify and classify diseases in cocoa pods. In this paper we introduce the development and evaluation of a deep learning computational model applied to the identification of diseases in cocoa pods, focusing on "monilia" and "black pod" diseases. An exhaustive review of state-of-the-art of computational models was carried out, based on scientific articles related to the identification of plant diseases using computer vision and deep learning techniques. As a result of the search, EfficientDet-Lite4, an efficient and lightweight model for object detection, was selected. A dataset, including images of both healthy and diseased cocoa pods, has been utilized to train the model to detect and pinpoint disease manifestations with considerable accuracy. Significant enhancements in the model training and evaluation demonstrate the capability of recognizing and classifying diseases through image analysis. Furthermore, the functionalities of the model were integrated into an Android native mobile with an user-friendly interface, allowing to younger or inexperienced farmers a fast and accuracy identification of health status of cocoa pods