Deep Learning-Based Computational Model for Disease Identification in
  Cocoa Pods (Theobroma cacao L.)
Darlyn Buenaño Vera Byron Oviedo Washington Chiriboga Casanova Cristian Zambrano-Vega
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