Disease Detection on Cocoa Crops Based on Computer-Vision Techniques: A Systematic Literature Review

Computer vision in the agriculture field aims to find solutions to guarantee and assure farmers the quality of their products. Therefore, studies to diagnose diseases and detect anomalies in crops, through computer vision, have been growing in recent years. However, crops such as cocoa required further attention to drive advances in computer vision to the detection of diseases. As a result, this paper aims to explore the computer vision methods used to diagnose diseases in crops, especially in cocoa. Therefore, the purpose of this paper is to provide answers to the following research questions: (Q1) What are the diseases affecting cocoa crop production? (Q2) What are the main Machine Learning algorithms and techniques used to detect and classify diseases in cocoa? (Q3) What are the types of imaging technologies (e.g., RGB, hyperspectral, or multispectral cameras) commonly used in these applications? (Q4) What are the main Machine Learning algorithms used in mobile applications and other platforms for cocoa disease detection? This paper carries out a Systematic Literature Review approach. The Scopus Digital, Science Direct Digital, Springer Link, and IEEE Explore databases were explored from January 2019 to August 2024. These questions have identified the main diseases that affect cocoa crops and their production. From this, it was identified that mostly Machine Learning algorithms based on computer vision are employed to detect anomalies in cocoa. In addition, the main sensors were explored, such as RGB and hyperspectral cameras, used for the creation of datasets and as a tool to diagnose or detect diseases. Finally, this paper allowed us to explore a Machine Learning algorithm to detect disease deployed in mobile and Internet of Things applications for detecting diseases in cocoa crops.