Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Chen, Jin; Liao, Yi; Zou, Rong; Zhang, Shuai; Xiaobo, Ning; Han, Mengna

doi:10.25165/j.ijabe.20201303.5478

Cited by 12 publications

(11 citation statements)

References 12 publications

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“…For mechanical damage in Figure 5a, the DT has 8 layers, 11 leaves, and 10 branches. The closer the decision point was to the top of the DT, the more significant the internal characteristics it represents (Chen et al., 2020). By calculating the minimum Gini index value of full wavelengths attributes, the wavelength 388.8 nm with the impurity value of 0.499 and Gini index value of 0.71 was selected as the standard of decision‐making root part class.…”

Section: Resultsmentioning

confidence: 99%

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

Qiu

Zeng

Shao

et al. 2022

Journal of Food Science

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The surface of carp is easily damaged during the descaling process, which jeopardizes the quality and safety of carp products. Damage recognition realized by manual detection is an important factor restricting the automation in the pretreatment. For the commonly used methods of mechanical and water‐jet descaling, damage area recognition according to the hyperspectral data was proposed. Two discrimination models, including decision tree (DT) and self‐organizing feature mapping (SOM), were established to recognize the damaged and normal descaling area with the average spectral value. The damage‐discrimination model based on DT was determined to be the optimal one, which possessed the best model performance (accuracy = 96.7%, sensitivity = 96.7%, specificity = 96.7%, F1‐score = 96.7%). Considering the efficiency and precision of damage‐area recognition and visualization, the principal component analysis (PCA) combined with pixel values statistical analysis was used to reduce the dimension of hyperspectral images at the image level. Through statistical analysis, the value 0 was used as the threshold to distinguish the normal area and the damaged area in the PC image to achieve preliminary segmentation. Then, the spectral values of the initially discriminated damage area were input into the DT discrimination model to realize the final discriminant of damaged area. On this basis, the position information of the damaged area could be used to realize the visualization. The final visualization maps for mechanical and water‐jet descaling damage were obtained by image morphology processing. The average recognition accuracy can reach 94.9% and 90.3%, respectively. The results revealed that the hyperspectral imaging technique has great potential to recognize the carp damage area nondestructively and accurately under descaling processing. Practical Application This study demonstrated that hyperspectral imaging technique can realize the carp damage area detection nondestructively and accurately under descaling processing. With the advantages of nondestructive and rapid, hyperspectral imaging system and the method can be widely expanded and applied to the quality detection of other freshwater fish pretreatment.

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Section: Resultsmentioning

confidence: 99%

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

Qiu

Zeng

Shao

et al. 2022

Journal of Food Science

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“…To obtain the kernel-MOG mixture of different moisture and impurity rates, the rice crop was harvested when the expected moisture and impurity rates were reached and then transported to the laboratory [21] . The collected materials were divided into three groups according to the moisture content: 10.4% (L), 19.6% (M), and 30.4% (H), and the 1000-grain weight of each group was measured manually.…”

Section: Test Methodsmentioning

confidence: 99%

“…In addition, the proportion of material components (kernels and MOGs) beneath the concave screen changes with the field environment and operation parameters when harvesting. As shown in Figure6a, the two piles on the left are thick and thin straw, most of which can generate signals by impacting the sensor plate, and the pile on the right are stalks, which can rarely generate signals because of their light weight.To obtain the kernel-MOG mixture of different moisture and impurity rates, the rice crop was harvested when the expected moisture and impurity rates were reached and then transported to the laboratory[21] . The collected materials were divided into three groups according to the moisture content: 10.4% (L), 19.6% (M), and 30.4% (H), and the 1000-grain weight of each group was measured manually.…”

mentioning

confidence: 99%

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Liao

Chen

Guan

et al. 2021

International Journal of Agricultural and Biological Engineering

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China has the world's largest planting area of paddy rice, but large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Reducing grain loss is an effective way to increase production and revenue. In this study, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for verifying the precision. The development of the monitoring system for grain loss included two stages: the first stage was to collect impact signals using a piezoelectric film, extract the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component), and identify the kernel impact signals using the J48 (C4.5) Decision Tree algorithm. In the second stage, the precision of the monitoring system was tested for the paddy rice at three different moisture contents (10.4%, 19.6%, and 30.4%) and five different grain/impurity ratios (1/0.5, 1/1, 1/1.5, 1/2, and 1/2.5). According to the results, the highest monitoring accuracy was 99.3% (moisture content 30.8% and grain/impurity ratio 1/2.5), the average accuracy of the monitoring tests was 92.6%, and monitoring of grain/impurity ratios between 1/1 and 1/1.5 (>95.4%) had higher accuracy than monitoring the other grain/impurity ratios. Monitoring accuracy decreased as impurities increased. The lowest accuracy for grain loss monitoring was obtained when the grain/impurity ratio was 1/2.5, with monitoring accuracies of 88.2%, 75.7% and 78.8% at moisture contents of 10.4%, 19.6% and 30.4%.

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“…The various models of maize cultivation and maize varieties and the different plant spacing in China, however, cause the adaptation between agricultural machinery and agronomy, and it is difficult to popularize mechanized maize harvest. There were still some problems such as high maize harvest loss rate, low stability, and poor adaptability of the maize harvester [8] .…”

Section: Introduction mentioning

confidence: 99%

Experiments and modeling of mechanism analysis of maize picking loss

Zhang

Chi

et al. 2021

International Journal of Agricultural and Biological Engineering

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Maize picking is the main form of maize harvest in China. Maize picking loss accounts for a large proportion of the current maize harvest loss. An experimental study and a theoretical analysis were conducted to explore the influencing factors and rules of maize picking loss. First, the boundary conditions, established by analyzing the mechanism of maize picking, determined the influences of maize picking loss. Then, single-factor experiments and a central composite design (CCD) method were used to determine the influence of various factors and their interactions on maize picking loss. Finally, the models of kernel loss and ear loss were set up to determine the optimal parameter combination of maize picking harvest. Field experiment verification was conducted. The results indicated that the optimal parameters of the maize picking harvest were the rotational speed of pulling rollers of 1120 r/min, operating speed of 1.94 m/s, the inclination of the header of 18° and clearance between the picking plates of 30 mm. By establishing these optimal parameters, the kernel loss rate was 0.065%, and the ear loss rate was 0%. The obtained experimental results and regression models could be used to predict the performance of the maize picking harvest, guide the adjustment of header working parameters, and provide a theoretical basis for reducing the mechanical loss of maize harvesting.

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Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Cited by 12 publications

References 12 publications

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Experiments and modeling of mechanism analysis of maize picking loss

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