Uniformity Detection for Straws Based on Overlapping Region Analysis

Ma, Junteng; Feng, Wenjiang; Xie, Huanxiong; Yang, Hongchen; Hu, Zhichao

doi:10.3390/agriculture12010080

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…However, satellite remote sensing has the disadvantages of being susceptible to weather, having long revisit periods, and having low spatial-temporal resolution, which makes it difficult to obtain higher resolutions. In studies based on agricultural RGB images, traditional machine vision detection methods [13][14][15][16] and deep learning methods [17,18] have been applied for the purposes of detecting the extent of straw return to the field. However, although this method can accurately calculate the straw cover rate by dividing the surface straw and determining the straw return grade of the plots, this method has been mainly adapted to the straw crush form.…”

Section: Introductionmentioning

confidence: 99%

Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification

Wang,

Gao,

Sun

et al. 2024

Agriculture

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Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection.

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Section: Introductionmentioning

confidence: 99%

Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification

Wang,

Gao,

Sun

et al. 2024

Agriculture

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Analysis of Influencing Factors and Operation Quality Evaluation Strategy of Straw Crushing and Scattering System

Wang

Feng

et al. 2022

Agriculture

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First, through a single factor test, it was determined that the conveying and feeding speed range was 0.9~1.5 m/s, the rotating speed range of straw chopper shaft was 1900~2300 r/min, and the moisture content of straw was 26~34%. Then the Box–Behnken experimental design method was adopted, with conveying and feeding speed, cutter shaft rotation speed, and straw moisture content as influencing factors; small and stable specific power consumption and maximum coverage uniformity are taken as evaluation indexes. The influence rules of various factors on operation indexes were analyzed, and response surface analysis was carried out. Further, the optimization function in Design-Expert12.0 was used to determine the optimal parameter combination as follows: Conveying and feeding speed of 0.8 m/s, rotating speed of cutter shaft of 2059.9 r/min, moisture content of straw of 30.7%, corresponding specific power consumption and coverage uniformity of 1163 J/s2 and 99.1%, respectively. Finally, the seeding system was mounted behind the straw crushing and scattering system for a field verification test. The results showed that when the crushing and scattering system of the whole machine operates with the design parameters, the specific power consumption was 1260 J/s2, the uniformity of straw coverage was 94.7%, and the error with the experimental value was less than 5%. The effect was better than the standard requirement. The test realized the crushing and uniform coverage of high-quality straw residues after rice harvest, which proved the scientific and reliable test-bed of the crushing and scattering system.

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Experimental Analysis and Evaluation of Automatic Control System for Evenly Scattering Crushed Straw

et al. 2023

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In order to improve the solution to the unachieved uniformity of straw throwing, the unachieved qualified rate of coverage and the uneven straw throwing in sowing wheat without a tillage process after the rice harvest, and to change this unsatisfied quality of the straw mulch, a set of automatic control systems for straw throwing and covering was designed innovatively. An STM32 microcontroller was used as the main control unit, and the torque-acquisition system was used to collect the torque of the cutter roller shaft in real time and convert it into the conveying signal of the crushed straw. The control system changes the conveying quantity of broken straw in real time, through the dynamic response. This process realizes the optimal dynamic matching between the conveying amount of crushed straw and the impeller speed. We set up two kinds of tests: a straw-crushing-and-throwing system test bench (T6)6 with an automatic control system and a control test bench (C) without an automatic control system. T1 to T5 are, in turn, 0.85 m/s, 1.0 m/s, 1.15 m/s, 1.30 m/s and 1.45 m/s. For the C test, six test levels of 0.85 m/s (C1), 1.0 m/s (C2), 1.15 m/s (C3), 1.30 m/s (C4), 1.45 m/s (C5) and variable speed test (C6) were also set as control tests. The running time of the test-bed at each test level was 10 s; taking the throwing uniformity of the crushed straw and the rate of coverage as indexes, the rapid effect of the throwing-impeller speed on the test indexes at six levels was studied, and compared with the control test. Based on the great practical needs of this problem, this experiment innovatively realized the automatic regulation of the rotating speed of the scattering impeller at different forward speeds. Although some experimental innovations have been made in this study, the smashing knife (group) of the knife roller shaft will hit the ground during the rotation, which brings uncertainty and certain experimental errors to the real-time monitoring of the torque signals. In the next step, more sensors and intelligent algorithms will be added to the system, to reduce the knife throwing.

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Uniformity Detection for Straws Based on Overlapping Region Analysis

Cited by 3 publications

References 27 publications

Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification

Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification

Analysis of Influencing Factors and Operation Quality Evaluation Strategy of Straw Crushing and Scattering System

Experimental Analysis and Evaluation of Automatic Control System for Evenly Scattering Crushed Straw

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