Influence of Computer Vision for prediction of harvest in high stem fruits: Systematic review

Resumen

The accelerated development of computer vision (AM) worldwide has a significant impact on fruit harvest estimation, leading to increased efficiency and a notable reduction in crop waste. This technology faces notable resistance to its use due to lack of knowledge in the agricultural sector. The objective of the work is to analyze artificial vision methods in predicting the harvest of high-stemmed fruits. Based on the inclusion and exclusion criteria, 26 open access articles from the Scopus, Scielo and Redalyc databases were chosen. The findings highlight that most studies used near-infrared (NIR) spectroscopy and RGB image processing to estimate harvest, achieving accuracies of 95% (citrus fruits) and 75% (apples) on average respectively. The use of UAVs with RGB and YoloV3 image sensors made it possible to achieve accuracies greater than 90%, enabling predictions made between 4 and 6 months before the harvest. It was concluded that the most used VA methods were RGB image sensor, spectrometry (NIR), unmanned aerial vehicles (UAV) and YOLOv3, which demonstrated accuracies greater than 75% in predicting the maturity of high-stemmed fruits. The choice of method will depend mainly on whether you want to analyze the internal or external part of the fruit, or both.