Finding the appropriate harvest time of coffee fruits using convolutional neural networks

Coffee pricing is strongly influenced by fruit quality. High-quality coffee fruits are achieved when developed under adequate nutritional, climatic, and health conditions but also by finding the appropriate harvest time. This paper aimed to present a methodological approach based on Convolutional Neural Networks (CNN) to identify the ripening process of coffee fruits in order to support farmers in the harvest decision. The study was conducted using images of coffee plants from farms located in Brazilian cities which were further labelled by several experts regarding its maturation stage as well as the appropriateness to harvest or not. Such images were later used in the learning and evaluation of state-of-the-art CNNs architecture models. The computer simulations results were satisfactory, with the model surpassing 92% accuracy, thereby achieving values higher than those of some current models. This approach can significantly improve harvest management by increasing the precision of fruit ripening classification systems and the predictability of crop evolution, thereby increasing both the added value of the product and reducing costs through quality management.