Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm

Malik, Muhammad H.; Zhang, Ting; Li, Han; Zhang, Man; Shabbir, Sana; Saeed, Ahmed Sardar M.

doi:10.1016/j.ifacol.2018.08.183

Cited by 67 publications

(33 citation statements)

References 12 publications

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“…Eighty-three percent of the mature test branches were harvested, but 1.4 attempts and 8 s were needed per branch. In Malik et al [27], an HSV transform was used to detect only red tomatoes. To separate the connected tomatoes, a watershed algorithm was used.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2020

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Protected agriculture is a field in which the use of automatic systems is a key factor. In fact, the automatic harvesting of delicate fruit has not yet been perfected. This issue has received a great deal of attention over the last forty years, although no commercial harvesting robots are available at present, mainly due to the complexity and variability of the working environments. In this work we developed a computer vision system (CVS) to automate the detection and localization of fruit in a tomato crop in a typical Mediterranean greenhouse. The tasks to be performed by the system are: (1) the detection of the ripe tomatoes, (2) the location of the ripe tomatoes in the XY coordinates of the image, and (3) the location of the ripe tomatoes’ peduncles in the XY coordinates of the image. Tasks 1 and 2 were performed using a large set of digital image processing tools (enhancement, edge detection, segmentation, and the feature’s description of the tomatoes). Task 3 was carried out using basic trigonometry and numerical and geometrical descriptors. The results are very promising for beef and cluster tomatoes, with the system being able to classify 80.8% and 87.5%, respectively, of fruit with visible peduncles as “collectible”. The average processing time per image for visible ripe and harvested tomatoes was less than 30 ms.

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Section: Literature Reviewmentioning

confidence: 99%

“…The computing time we achieved for the processing (identification and location) of an image was of the order of milliseconds, while in other works [18,24,27], it was of the order of seconds; 3.…”

mentioning

confidence: 91%

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

et al. 2020

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“…The study of Oppenheim, Edan, & Shani (2017) utilized the HSV shading space to segment the tomato flower from the contextual. In the study of Malik et al (2018), new mature tomato detection algorithm based on the improved HSV color space watershed segmentation was proposed to guide a robot to pick up red tomatoes automatically. Same utilization of HSV features was used by Kandi (2010) designed for automated defect detection and sorting of single-color fruits such as banana, tomatoes, and mangoes.…”

Section: Introductionmentioning

confidence: 99%

Tomato Growth Stage Monitoring for Smart Farm Using Deep Transfer Learning with Machine Learning-based Maturity Grading

Luna

Dadios²,

Vicerra³

2020

Agrivita.J.Agr.Sci

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The tomato farming industry needs to adopt new ideas in applying the technology for its growth monitoring and main. Machine vision and image processing techniques have become useful in the increasing need for quality inspection of fruits, particularly, tomatoes. This paper deals with the design and development of a computer-vision monitoring system to assess the growth of tomato plants in a chamber by detecting the presence of flowers and fruits. The system also provides maturity grading for the tomato fruit. Two pre-trained deep transfer learning models were used in the study for the detection of flowers and fruits, namely, the Regional-based Convolutional Neural Network (R-CNN) and the Single Shot Detector (SDD). Maturity classification of tomato fruits are implemented using the Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and the Support Vector Machine (SVM). Evaluation results show that for the detection of flowers and fruits, the overall accuracy of the R-CNN is 1.67% for flower detection and 19.48% for the fruit detection while SSD registered 100% and 95.99% for flower and fruit detection respectively. In the machine learning for maturity grading, SVM produced the training-testing accuracy rate of 97.78%-99.81%, KNN with 93.78%-99.32%, and ANN with 91.33%-99.32%.

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“…It is difficult to solve problems such as overlap and occlusion, and it is easily affected by lighting conditions. The effect is poor in a complex background, resulting in relatively high cost and poor applicability of these methods [ 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model

Luo

Zhang

2020

Sensors

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The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the important methods for evaluating the yield of Korean pine forests. In recent years, there has been a certain number of detection accuracy for image processing of fruits in trees using deep-learning methods, but the overall performance of these methods has not been satisfactory, and they have never been used in the detection of pine cones. In this paper, a pine cone detection method based on Boundary Equilibrium Generative Adversarial Networks (BEGAN) and You Only Look Once (YOLO) v3 mode is proposed to solve the problems of insufficient data set, inaccurate detection result and slow detection speed. First, we use traditional image augmentation technology and generative adversarial network BEGAN to implement data augmentation. Second, we introduced a densely connected network (DenseNet) structure in the backbone network of YOLOv3. Third, we expanded the detection scale of YOLOv3, and optimized the loss function of YOLOv3 using the Distance-IoU (DIoU) algorithm. Finally, we conducted a comparative experiment. The experimental results show that the performance of the model can be effectively improved by using BEGAN for data augmentation. Under same conditions, the improved YOLOv3 model is better than the Single Shot MultiBox Detector (SSD), the faster-regions with convolutional neural network (Faster R-CNN) and the original YOLOv3 model. The detection accuracy reaches 95.3%, and the detection efficiency is 37.8% higher than that of the original YOLOv3.

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Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm

Cited by 67 publications

References 12 publications

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

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

Tomato Growth Stage Monitoring for Smart Farm Using Deep Transfer Learning with Machine Learning-based Maturity Grading

Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model

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