Abstract
The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.
Highlights
The real-time information acquisition and monitoring growth status of tomato young fruits can grasp the early quality information of young fruits, and it is of great significance to timely remove abnormal fruits having deformities, diseases, insects, etc., to ensure the normal growth of healthy fruits and to improve fruit quality and yield [1]
Zhao et al extracted the Haar-like features of tomato grayscale images and used the Ada Boost classifier to identify the fruits, and eliminated false positives in the classification results through the color analysis method based on Average Pixel Value (APV) [8]
In response to the above problems, this paper proposes a method for detecting tomato young fruits in a near color background based on improved Faster R-Convolutional Neural Network (CNN) with an attention mechanism
Summary
The real-time information acquisition and monitoring growth status of tomato young fruits can grasp the early quality information of young fruits, and it is of great significance to timely remove abnormal fruits having deformities, diseases, insects, etc., to ensure the normal growth of healthy fruits and to improve fruit quality and yield [1]. Zhao et al extracted the Haar-like features of tomato grayscale images and used the Ada Boost classifier to identify the fruits, and eliminated false positives in the classification results through the color analysis method based on Average Pixel Value (APV) [8]. The method mentioned above is not sensitive to small-sized, near color background tomato young fruits, it is difficult to achieve a stable recognition effect. It is difficult to achieve high-precision and real-time requirements for the recognition of tomato young fruits with near color background using traditional methods. In response to the above problems, this paper proposes a method for detecting tomato young fruits in a near color background based on improved Faster R-CNN with an attention mechanism. Ithneonrdetewr otorkavaoccidorodvinerg-ftiotttihneg cphraorbalcetmersi,sttihces torfatnhsefedralteaarsnetining tmheisthpoadpeisr.used to fine-tune the network according to the characteristics of the data set in this paper
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