Convolutional Neural Network for the Detection of Cocoa Maturity with an Approach for the Analysis of Images Captured at Different Distances

This study presents an approach to improve cocoa harvesting using image recognition technology. Convolutional neural networks (CNN) along with the help of the Roboflow platform were used to analyze images of cocoa fruits and determine their maturity stage in an uncontrolled environment. The study focused mainly on the effectiveness of taking pictures at different distances, dividing the images into three categories (0.10m-0.30m, 0.30m-1.00m and 1.00m-3.00m) each trained with 400 images in order to evaluate the performance of each one in terms of its mAP, precision and recall. Subsequently, a fourth neural network was developed using all the data collected, with a total of 1,200 images used for training, among which 3,255 unripe cocoa fruits and 2,555 ripe cocoa fruits were found. The results obtained were a mAP of 92.3%, accuracy of 91.6% and recall of 85.1%, demonstrating the high accuracy of the model in the classification of cocoa fruits. This neural network has the potential to improve the quality of the final product by accurately determining the state of maturity of the cocoa, which is essential for the industry.