Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD

Abstract

The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection.

Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse

“…In the most recent SoA, the interest in DL strategies has been growing [28][29][30][31][32][33]. This interest is due to the higher computability rate of the most recent computers and new edge computing devices dedicated to running DL models, as the TPU.…”

“…In recent years, machine learning, and especially deep learning, techniques in fruit detection has been increasingly used and tested [8,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Unlike conventional methods, machine learning is a more robust and accurate alternative with a better response to problems such as occlusion and green tomato detection.…”

“…The fruit detection accuracy of the R-CNN model was only 19.48%, while the SSD model showed a much higher detection rate of 95.99%. To detect cherry tomatoes in greenhouses, whether ripe, green or intermediate, Yuan et al [32] developed an SSD-based algorithm. After building the datasets, they were used to train and develop network models.…”

Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting

“…Depending on the type of fruit, the precision of this method varied between 0.75 and 0.92. Yuan et al [30] proposed a method for cherry tomato detection based on a CNN for reducing the influence of illumination, growth difference and occlusion. Yoshida et al [31] obtained 3D images of the bunch tomato crops and detected the position of the bunch peduncle.…”

Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

“…The main function of the vision system is to accurately identify the target fruit and provide information for motion control [7,8]. However, under the complex natural environment, orchards experience constantly changing weak and strong illumination conditions [9,10]. Depending on the intensity of illumination, different degrees of shadows are formed on the surface of the apples, because of the occlusion caused by fruit branches and leaves as well as clusters of neighboring fruits.…”