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.

Computer vision applications for detecting diseases in agriculture have been gaining relevance in recent years through the use of deep learning architectures. Digital image datasets serve as the main input for these architectures, enabling the analysis of patterns associated with a specific disease. However, some diseases have not yet been explored due to the limited availability of annotated image datasets. Cocoa pods are fundamental for the production of chocolate and its derived products; nevertheless, their production is threatened by Monilia roreri, a fungal disease responsible for yield losses of approximately 30% - 40%. Therefore, this paper proposes a CocoaMoniliaDataSet, a dataset of cocoa pods labeled across symptomatic stages of Monilia disease. Although the infection of cocoa pod caused by Monilia roreri describes four biological cycles, the dataset takes the visual symptoms into three classes to support computer vision task. These symptoms correspond to: cycle 1 (humps), cycle 2 (brown/oily spot and cycle 3 (white powder or sporulation). In this paper, cycle 2 represents a symptomatic stage that merges the second and third biological cycles. In addition, the dataset included healthy cocoa pods to facilitate early detection of the disease. The dataset comprises 1953 images with four labeled classes: (1) healthy cocoa pod labeled as (h0); (2) first Monlilia cycle, humps, labeled as (m1); (3) second - third Monilia cycle of the disease labeled as (m2); and (4) fourth Monilia cycle labeled as m3. Each instance in the image was annotated using the polygon method in CVAT (Computer Vision Annotation Tool), and the resulting labels are provided in COCO 1.0, YOLO, and segmentation mask 1.1 format to enable training object detection algorithms using bounding boxes. The publication of this dataset is essential for exploring techniques to diagnose cocoa disease using computer vision techniques.

Cocoa production is a major economic driver across Latin America, yet it is severely threatened by Frosty Pod Rot, caused by the fungal pathogen Moniliophthora roreri, which can cause yield losses of up to 90% in affected plantations. Conventional disease management relies on manual visual inspection, which is subjective and typically detects damage only after visible symptoms appear, while public annotated multispectral datasets to support earlier, image-based detection remain scarce. To address this gap, we present a high-resolution multispectral image dataset of cocoa pods (Theobroma cacao L.) captured under real field conditions in Colombia. The dataset comprises 1032 images (4000 × 3000 px, JPG) across two spectral configurations, Red Edge (RE, 441 images) and Red-Green-Near Infrared (RGN, 591 images), collected during three field campaigns across the CNCH12, CNCH13, and SCI-1 cocoa clone varieties. A total of 1688 pod instances were manually annotated at pixel level in CVAT into four pathological classes reflecting disease progression — Healthy, Latent, Spots, and Sporulation — and released in COCO and YOLO formats. The dataset is partitioned into training, validation, and testing subsets, with a geometric augmentation pipeline applied to the training split to balance underrepresented disease classes, expanding it by an additional 1820 images. The complete 8.2 GB dataset is openly hosted on Zenodo. This resource supports the development and benchmarking of computer vision models for early disease detection and enables direct comparison between multispectral and conventional RGB images in tropical agriculture.