Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning

Ji, Jiangtao; Sang, Yayuan; He, Zhitao; Jin, Xin; Wang, Shengsheng

doi:10.1371/journal.pone.0254544

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…Its long range provides support for rapid decision making before a difficult turn is to be made. This would give the tractor the ability of rapid observation in detecting variations along its way [141]. However, the challenge in detecting the centimeter-level variation at a close-range path boundary or an obstacle is still present.…”

Section: Ground-based Platformsmentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Section: Ground-based Platformsmentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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“…Corn is a crop that is grown for human consumption as well as for animal feed, the production of biofuel, and industrial uses. Corn planting requires both high quality and high speed because corn has a large planting area, a short planting period, and the quality of the planting directly affects the yield [2]. A typical seed guide tube is designed for low-speed operation and has a straightforward structure.…”

Section: Introductionmentioning

confidence: 99%

Research on Receiving Seeds Performance of Belt-Type High-Speed Corn Seed Guiding Device Based on Discrete Element Method

Tao

et al. 2023

Agriculture

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Because the initial speed of the seeds leaving the seed disk is too high, they collide and bounce off the inner wall of the seed guide tube, resulting in poor sowing quality when corn is sown at high speeds above 12 km/h. This study clarifies the primary factors affecting the stability of seed receiving and the accuracy of the seed entering the seed cavity, establishes the dynamic model of seed clamping, transportation, and releasing, and investigates the belt-type high-speed corn seed guiding device with the seed receiving system as the research object. It also proposes an improved method of adding herringbone lines on the finger surface to address this issue. Using EDEM software, a virtual experiment of seed-receiving performance was conducted, and the change trend of stress on seeds with and without a herringbone pattern and different wheel center distance as well as the change trend of the speed of seeds with various feeder wheel speeds and finger length, were both examined. The outcomes of the simulation demonstrate that the herringbone-lined feeder wheel could increase the stress on seeds. The average value of the stress on the seeds is the highest at the wheels’ center distance of 37 mm. The stability and speed fluctuation of seeds introduced into the seed cavity were better when the feeder wheel speed was 560 r/min. The speed of fluctuation and stability of the seeds introduced into the seed cavity were better when the finger length was 12 mm. The high-speed camera test on the test bench was used to verify the seed guiding process in accordance with the simulation results, and the outcomes were largely consistent. The study’s findings can serve as a theoretical foundation for a belt-type high-speed corn seed guiding device optimization test.

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“…This center subsequently analyzes individual seed data to evaluate the seeding performance. It is worth noting that the machine vision approach necessitates a conducive detection environment, which is typically found in laboratory settings [20][21][22][23][24][25][26]. The aforementioned methods cannot achieve the online detection of the uniformity, accuracy, and dispersion of the seeder.…”

Section: Introductionmentioning

confidence: 99%

An Online Method for Detecting Seeding Performance Based on Improved YOLOv5s Model

Zhao,

Xi,

Shi

et al. 2023

Agronomy

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Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion.

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Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning

Cited by 10 publications

References 16 publications

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Research on Receiving Seeds Performance of Belt-Type High-Speed Corn Seed Guiding Device Based on Discrete Element Method

An Online Method for Detecting Seeding Performance Based on Improved YOLOv5s Model

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