Detection the maturity of multi-cultivar olive fruit in orchard environments based on Olive-EfficientDet

Abstract

Olive (Olea europaea L.) fruit maturity detection in orchard environments plays a vital role in ensuring the quantity and quality of olive oil. However, it is difficult for olive fruit maturity detection in orchard environments due to the minimal phenotypic difference between neighboring maturity, and the occlusion and overlap of olive fruits. Most existing research pays more attention to olive fruit detection while ignoring olive fruit maturity detection problems. Therefore, a novel Olive-EfficientDet is proposed to detect the maturity of multi-cultivar olive fruits in orchard environments. In Olive-EfficientDet, the convolutional block attention module (CBAM) is rationally embedded into the backbone network for distinguishing different maturity-stage olive fruits with higher accuracy. The improved weighted bi-directional feature pyramid network (Bi-FPN) structure head network is constructed to focus on occlusion and overlap olive fruits, which can fully fuse semantic relationships and location information of different layers. The experimental results showed that the proposed Olive-EfficientDet provides an effective method for olive fruit maturity detection in orchard environments. The mean average precision (mAP) of fruit maturity detection reached 94.60%, 95.45%, 93.75%, and 96.05% for olives of cultivar ‘Frantoio’, ‘Ezhi 8′, ‘Leccino’, and ‘Picholine’; the mean detection time was 337 ms per image; and the model size was only 32.4 MB. In addition, the Olive-EfficientDet exhibits robust adaptability to complex illumination, occlusion, and overlap in uncontrolled and challenging orchard environments. Comparative experiments were conducted using Olive-EfficientDet and other state-of-the-art fruit maturity detection methods. The comparative experiment results showed that the mAP of the olive fruit maturity detection with four cultivars using Olive-EfficientDet was higher than that of SSD, EfficientDet, YOLOv3, and Faster RCNN. Especially, Olive-EfficientDet obtained the highest mAP for olive fruit maturity detection in orchard environments while maintaining an encouraging model size and speed, which can provide a technical foundation for fruit maturity detection in olive harvesting robots.