A backlight and deep learning based method for calculating the number of seeds per silique

Zhao, Youjun; Wu, Wei; Zhou, Yuzhuang; Zhu, Bo; Yang, Tianle; Yao, Zhaosheng; Ju, Chengxin; Sun, Chengming; Liu, Tao

doi:10.1016/j.biosystemseng.2021.11.014

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Although X-ray imaging is effective at distinguishing filled and unfilled grains, the radiation may affect the nutrients and genes of seeds, creating problems in seed breeding ( Yali and Mitiku, 2022 ). Deep learning techniques have also been used to identify shapes and calculate the number of seeds ( Uzal et al, 2018 ; Zhao et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Size measurement and filled/unfilled detection of rice grains using backlight image processing

Feng,

Wang,

Zeng

et al. 2023

Front. Plant Sci.

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Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.

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Section: Introductionmentioning

confidence: 99%

Size measurement and filled/unfilled detection of rice grains using backlight image processing

Feng,

Wang,

Zeng

et al. 2023

Front. Plant Sci.

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“…For example, Lins et al [ 20 ] designed a method for counting fish based on image density level classification and local regression. Zhao et al [ 21 ] used the DeepLabV3+ network to achieve a recall rate of 91% for counting the number of seeds per silique in long-horned fruit. Gao et al [ 22 ] created an automatic apple counting method using the YOLOv4-tiny detection network and a single-object tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model

Huang

Xiao

Liu

et al. 2023

Sensors

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Pig counting is an important task in pig sales and breeding supervision. Currently, manual counting is low-efficiency and high-cost and presents challenges in terms of statistical analysis. In response to the difficulties faced in pig part feature detection, the loss of tracking due to rapid movement, and the large counting deviation in pig video tracking and counting research, this paper proposes an improved pig counting algorithm (Mobile Pig Counting Algorithm with YOLOv5xpig and DeepSORTPig (MPC-YD)) based on YOLOv5 + DeepSORT model. The algorithm improves the detection rate of pig body parts by adding two different sizes of SPP networks and using SoftPool instead of MaxPool operations in YOLOv5x. In addition, the algorithm includes a pig reidentification network, a pig-tracking method based on spatial state correction, and a pig counting method based on frame number judgment on the DeepSORT algorithm to improve pig tracking accuracy. Experimental analysis shows that the MPC-YD algorithm achieves an average precision of 99.24% in pig object detection and an accuracy of 85.32% in multitarget pig tracking. In the aisle environment of the slaughterhouse, the MPC-YD algorithm achieves a correlation coefficient (R2) of 98.14% in pig counting from video, and it achieves stable pig counting in a breeding environment. The algorithm has a wide range of application prospects.

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“…At present, DeepLabV3+ is widely used in grain target detection. For example, Zhao et al realized the segmentation and counting of rapeseed [ 15 ]; Zhang et al realized the automatic extraction of wheat lodging area [ 16 ]; Bhagat et al realized the plant leaf segmentation and counting [ 17 ]; and Yang et al achieved the efficient segmentation of soybean planting areas [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Online Detection System for Wheat Machine Harvesting Impurity Rate Based on DeepLabV3+

Chen

Jin

et al. 2022

Sensors

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Wheat, one of the most important food crops in the world, is usually harvested mechanically by combine harvesters. The impurity rate is one of the most important indicators of the quality of wheat obtained by mechanized harvesting. To realize the online detection of the impurity rate in the mechanized harvesting process of wheat, a vision system based on the DeepLabV3+ model of deep learning for identifying and segmenting wheat grains and impurities was designed in this study. The DeepLabV3+ model construction considered the four backbones of MobileNetV2, Xception-65, ResNet-50, and ResNet-101 for training. The optimal DeepLabV3+ model was determined through the accuracy rate, comprehensive evaluation index, and average intersection ratio. On this basis, an online detection method of measuring the wheat impurity rate in mechanized harvesting based on image information was constructed. The model realized the online detection of the wheat impurity rate. The test results showed that ResNet-50 had the best recognition and segmentation performance; the accuracy rate of grain identification was 86.86%; the comprehensive evaluation index was 83.63%; the intersection ratio was 0.7186; the accuracy rate of impurity identification was 89.91%; the comprehensive evaluation index was 87.18%; the intersection ratio was 0.7717; and the average intersection ratio was 0.7457. In terms of speed, ResNet-50 had a fast segmentation speed of 256 ms per image. Therefore, in this study, ResNet-50 was selected as the backbone network for DeepLabV3+ to carry out the identification and segmentation of mechanically harvested wheat grains and impurity components. Based on the manual inspection results, the maximum absolute error of the device impurity rate detection in the bench test was 0.2%, and the largest relative error was 17.34%; the maximum absolute error of the device impurity rate detection in the field test was 0.06%; and the largest relative error was 13.78%. This study provides a real-time method for impurity rate measurement in wheat mechanized harvesting.

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A backlight and deep learning based method for calculating the number of seeds per silique

Cited by 11 publications

References 25 publications

Size measurement and filled/unfilled detection of rice grains using backlight image processing

Size measurement and filled/unfilled detection of rice grains using backlight image processing

An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model

Online Detection System for Wheat Machine Harvesting Impurity Rate Based on DeepLabV3+

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