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

翻译：可可豆荚的早期病害识别是保障优质可可生产的重要任务。利用机器学习、计算机视觉和深度学习等人工智能技术，为可可豆荚病害的识别与分类提供了有前景的解决方案。本文介绍了一种应用于可可豆荚病害识别的深度学习计算模型的开发与评估，重点关注"念珠菌病"和"黑荚病"。我们基于计算机视觉和深度学习技术在植物病害识别领域的相关科学论文，对现有计算模型进行了详尽综述。通过文献调研，最终选定了EfficientDet-Lite4这一高效轻量级目标检测模型。利用包含健康与患病可可豆荚图像的数据集，训练模型以较高精度检测和定位病害表现。模型训练与评估的显著改进表明，该模型具备通过图像分析识别与分类病害的能力。此外，模型功能已集成至具有用户友好界面的原生安卓移动应用中，使年轻或经验不足的农户能够快速准确地识别可可豆荚的健康状况。