In-field machine vision system for identifying corn kernel losses

Monhollen, Nolan S.; Shinners, K. J.; Friede, Joshua C.; Rocha, Eduardo Melani; Luck, Brian D.

doi:10.1016/j.compag.2020.105496

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…The correctly identified intact corn kernels are marked as TP, the incorrectly identified are marked as FN; meanwhile, the correctly identified broken kernels are marked as TN and the incorrectly identified marked as FP. According to Equation (7), the precision, recall, and F1 score are calculated as 92.8%, 93.5%, and 93.11%, respectively. The F1 score combines the results of precision and recall; when F1 is higher, it shows that the yolov4-tiny model is more effective.…”

Section: Laboratory Experiments Resultsmentioning

confidence: 99%

“…The precision and recall have four states after the test sample is predicted: true positive (TP), false positive (FP), true negative (TN), and false negative (FN). The definition of these indexes is shown in Equation (7). The sample dividing threshold is 50%.…”

Section: Evaluation Of the Yolov4-tiny Modelmentioning

confidence: 99%

“…Flores et al [5] adopted several CNN algorithms to learn and distinguish RGB and CIR images of corn and soybean, and it was found that GoogLeNet is an effective discrimination method with an accuracy of 99.9% and calculation speed of 20 ms. Rasmussen et al [6] employed Faster R-CNN to detect broken corn silage particles after harvesting; the results showed that the detection accuracy was 45.2%, which could identify large particles from miscellaneous ones. Monhollen et al [7] developed a corn kernel loss rate detection program based on Faster R-CNN, which achieved an average accuracy of 0.90, the additional field tests obtained the accuracy of 0.91. Suo et al [8] used yolov4 to study the transfer of kiwifruit detection, and obtained the highest mAP of 91.9% with an image processing speed of 25.5 ms. Zhang et al [9] proposed a water-meter pointerreading recognition method based on improved yolov4; the detection accuracy of this method reached 98.68%, which indicated that the lightweight algorithm could quickly and accurately identify targets.…”

Section: Introductionmentioning

confidence: 99%

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Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Yao

et al. 2021

Agriculture

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At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

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Section: Laboratory Experiments Resultsmentioning

confidence: 99%

Section: Evaluation Of the Yolov4-tiny Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Yao

et al. 2021

Agriculture

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show abstract

“…Zhang et al designed a low-injury corn picking mechanism and verified through experiments that the primary and secondary order of influencing the damage rate of the cob was the picking roller speed, spring stiffness, and forward speed [3]. Monhollen, NS developed a corn grain loss assessment system comprised of a machine vision image system to evaluate cutter seed loss and harvest loss [4]. These studies have achieved good research on the factors influencing the loss of the cutting table and the determination of the loss rate, but most of these studies have only obtained the influence law of individual factors and the optimal combination parameters, and there is a lack of research on how the harvest quality of the cutting table can be improved and the specific measures to reduce the harvest loss of the cutting table.…”

Section: Introductionmentioning

confidence: 99%

Development and Testing of Automatic Row Alignment System for Corn Harvesters

Geng

Liu

et al. 2022

Applied Sciences

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Corn harvester row alignment is a critical factor to improve harvesting quality and reduce cob drop loss. In this paper, a corn harvester automatic row alignment system is designed with a self-propelled corn combine harvester, incorporating a front-end touch row alignment mechanism of the cutting table and a harvester steering control system. Corn stalk lateral deviation from the reference row during harvesting is detected by a front-end touch-row alignment mechanism that serves as an input to the automatic alignment system. The harvester steering control system consists of Hirschmann PLC controller, electric steering wheel, steering wheel deflection angle detection device, display module and mode selection module, etc. The adaptive fuzzy PID control algorithm is used to determine the desired turning angle of the harvester steering wheel by combining with the harvester kinematic model, and the model is simulated and analyzed by Matlab/Simulink software. The automatic row alignment system was mounted on a 4LZ-8 self-propelled corn harvester for field tests, and the test results showed that the average percentage of deviation of corn stalks from the center of the row alignment cutting path within ±15 cm during the automatic row alignment process was 95.4% at harvester speeds ranging from 0 to 4.6 km/h, which could meet the requirements of the corn harvester for row alignment harvesting. The test results meet the requirements of the corn harvester for row alignment and serve as a benchmark for the research on automatic row alignment of corn harvesters.

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“…Improving the operating quality and efficiency of harvesters could reduce the loss of corn kernels and profit loss caused by harvesters. Therefore, some studies have been carried out to improve the operating quality and efficiency of harvesters [15][16][17][18]. Wan et al used computer vision technology to recognize the shape of corn kernels and constructed a BP neural network with grain shape feature parameters to detect the grain shape of corn kernels and determine the rate of broken kernels [19].…”

Section: Introductionmentioning

confidence: 99%

Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters

Zhou

Xin

et al. 2021

Applied Sciences

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This study analyzed the engine operating condition curve of the corn kernel harvester. Field experiments identified the feed rate, concave clearance, and cylinder speed as the main factors affecting operating quality and efficiency. A ternary quadratic regression orthogonal center-of-rotation combined optimization test method was used to determine the feed rate, cylinder speed, and concave clearance as the influencing factors, and the engine speed variation rate, crushing rate, impurity rate, loss rate, and cylinder speed variation rate as the objective functions. A mathematical regression model was developed for the combination of operating quality indicators, efficiency indicators, and operating parameters of the corn kernel harvester. A non-linear optimization method was used to optimize the parameters of each influencing factor. The results showed that with a feed rate of 12 kg/s, a forward speed of 5 km/h, a cylinder speed of 360 r/min, and a concave clearance of 30 mm, the average crushing rate was 3.91%, the average impurity rate was 1.71%, and the kernel loss rate was 3.1%. This model could be used for the design and development of intelligent control systems.

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In-field machine vision system for identifying corn kernel losses

Cited by 10 publications

References 11 publications

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Development and Testing of Automatic Row Alignment System for Corn Harvesters

Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters

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