Abstract
Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.
Highlights
There is a vital need in the horticulture research field to understand fruit-related phenotypic traits, such as fruit number, size, and color
Object detection datasets The following source fruit dataset and target fruit dataset were used in the fruit detection experiments: (1) Source orange dataset: The dataset was collected from an orange orchard in Sichuan Province, China
This paper proposed a new solution to overcome the current problem of high labeling cost for training data acquisition: the automatic labeling of unlabeled fruit datasets
Summary
There is a vital need in the horticulture research field to understand fruit-related phenotypic traits, such as fruit number, size, and color. An object detection technique can obtain the location and category information of the fruit in the image, such as fruit positioning[1,2], fruit estimation[3,4], and automatic fruit picking[5,6], which is the technical basis for intelligent work in the orchard. Owing to the advantages of deep learningbased object detection techniques[7,8,9,10,11,12,13], which perform. Most related works use a strongly supervised labeling method[15] that requires drawing bounding boxes around the target objects with location and category information for model training. Zhang et al Horticulture Research (2021)8:119 bounding boxes manually. The strongly supervised labeling method provides better detection performance, the labeling cost were high and time-consuming
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