Fast detection of banana bunches and stalks in the natural environment based on deep learning

Abstract

With the widespread application of machine vision technology in agriculture, the intelligent management of banana orchards is urgent. Accurate detection of banana bunches and stalks is a precondition for orchard yield estimation and automatic harvesting. In a complex banana orchard environment, banana bunches and stalks are similar to the leaves in color, and banana stalks are similar to the petiole in texture, making the detection of banana bunches and stalks in banana orchards challenging. This study proposes an accurate and fast multiclass detection method for banana bunches and stalks. A regular RGB camera was used to collect images. The well-known YOLOv4 network was used to detect the banana bunches and stalks, and the input image resolution was discussed by training and comparison. The banana bunch and stalk detection model showed excellent reliability and generalization ability in different illumination and occlusion scenarios. The AP of the banana bunch and stalk detection was 99.55% and 87.82%, respectively, and the mAP of the detection model was 93.69%. The average execution time was 44.96 ms. The detection of small-sized banana bunches and stalks was discussed, and its significance in banana orchard applications was analyzed. The experimental results show that the fast real-time detection of banana bunches and stalks in the natural environment is helpful for the intelligent management of banana orchards.