Pineapples Health Detection Using Deep Learning Models

Abstract

Early detection of diseases in agricultural crops remains a significant challenge, especially for small producers who still rely on manual visual inspections—time-consuming, subjective processes with low reproducibility. In pineapple (Ananas comosus) cultivation, phytosanitary monitoring is essential to prevent losses associated with diseases such as fusarium wilt, wet rot, and wilt, as well as infestations by pertussis and nutritional deficiencies. This study presents an innovative automated monitoring approach for detecting visual anomalies in pineapple fruits, based on computer vision techniques applied primarily to images obtained under real field conditions, considering variations in lighting, occlusions, leaf overlap, different fruit orientations, and background interference. Furthermore, a comparative analysis was conducted between different versions of the You Only Look Once model (YOLOv5, YOLOv8, and YOLOv9) to identify the most suitable solution for different production profiles—from small family farms with limited computational resources to large farms with advanced technological infrastructure. The evaluation considered the metrics of Precision, Recall, Average Precision (mAP), and inference time, as well as operational feasibility in low-infrastructure contexts. The results demonstrate consistent performance among the tested models, with YOLOv8 standing out as the most suitable alternative for practical applications in the field and in embedded devices, establishing itself as a robust, accessible, and efficient solution for automated phytosanitary monitoring of pineapple crops.