A fuzzy decision-making algorithm-based header height measurement system for combine harvester

Measurement, Год журнала: 2025, Номер unknown, С. 116918 - 116918

Опубликована: Фев. 1, 2025

Язык: Английский

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Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

Agriculture, Год журнала: 2024, Номер 14(10), С. 1846 - 1846

Опубликована: Окт. 19, 2024

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and stubble color interference in field, make it challenging to identify harvest accurately quickly. Therefore, this paper proposes recognition model based on MV3_DeepLabV3+ network framework, which can quickly complete identification environments. uses lightweight MobileNetV3_Large backbone LeakyReLU activation function avoid neural death problem. Depth-separable convolution introduced into Atrous Spatial Pyramid Pooling (ASPP) reduce complexity parameters. cubic B-spline curve-fitting method extracts line. A prototype harvester was built, field tests were conducted. test results show that proposed achieves segmentation accuracy 98.04% unharvested regions environments, with IoU 95.02%. When travels at 0~1.5 m/s, normal speed operation, average processing time pixel error single image are 0.15 s 7.3 pixels, respectively. This could achieve high fast speed. provides practical autonomous operation

Язык: Английский

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Technological development and optimization of pushing and grasping functions in robot arms: A review

Measurement, Год журнала: 2024, Номер unknown, С. 115729 - 115729

Опубликована: Сен. 1, 2024

Язык: Английский

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A stereoscopic video computer vision system for weed discrimination in rice field under both natural and controlled light conditions by machine learning

Measurement, Год журнала: 2024, Номер 237, С. 115072 - 115072

Опубликована: Июнь 12, 2024

A site-specific weed detection and classification system was implemented with a stereoscopic video camera to reduce the adverse effects of chemical herbicides in rice field. computer vision meta-heuristic hybrid NN-ICA classifier were used accurately discriminate between two varieties plants, under either natural light (NLC) or controlled conditions (CLC). Preprocessing, segmentation, matching procedures performed on images coming from right left channels. Most discriminant features selected average, arithmetic geometric, using NN-PSO algorithm. Accuracy results stereo NLC 85.71 % for mean (AM) 85.63 geometric (GM), test set. At same time, accuracy CLC reached 96.95 AM case 94.74 GM case, being consistently higher than those NLC.

Язык: Английский

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Digital twin for monitoring threshing performance of combine harvesters

Measurement, Год журнала: 2024, Номер 239, С. 115411 - 115411

Опубликована: Июль 29, 2024

Язык: Английский

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