Agricultural Unmanned Systems: Empowering Agriculture with Automation

doi:10.3390/books978-3-7258-1619-4

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https://mdpi.com/books/pdfview/book/9529

Contributor(s)

Wang, Shubo (editor)

Language

English

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Abstract

Automation is crucial for the advancement of modern agriculture. It has a significant role in enhancing production efficiency and output, reducing labor costs, addressing natural disasters, and boosting sustainability. Automation utilizes big data and artificial intelligence to monitor agricultural production. It introduces new farming models that adapt to the challenges of scalability and environmental changes, achieving precise and efficient agricultural development. This Special Issue, titled “Agricultural Unmanned Systems: Empowering Agriculture with Automation”, focuses on sharing knowledge related to integrated and precise operational agriculture systems in the sky, air, land, and water. It explores intelligent sensing and control technologies in smart agricultural unmanned systems to advance the progress of unmanned agriculture. Establishing global demonstration sites is essential. These sites support the revolutionary advancement of smart agricultural machinery in automated, intelligent, unmanned, cluster operations.

URI

https://directory.doabooks.org/handle/20.500.12854/143790

Keywords

picking robots; robotic configurations; dual-manipulator; design optimization; high-power tractors; subsoiling operation; multiple factors; joint-control method; air-assisted sprayer; behind canopies; droplet loss; foliage area volume density (FAVD); environmental pollution; orchard; lidar; obstacle detection; harvester; pre-collision system; baler; feed rate; pickup platform; working power; frequency domain filtering; model; digital image processing; traditional machine learning; harvesting robot; computer vision; object detection; object recognition; research overview; research review; Rose Oxide (4-Methyl-2-(2-methyl-1-propenyl) tetrahydropyran); de-aromatic wine; NIR spectroscopy; Si-PLSR; wavebands analysis; deep learning; fruit detection; fruit recognition; automatic harvesting; current challenge; development trend; transfer learning; MobileNetV3; pest detection; embedded system; citrus pest; alfalfa seeds; uniform fabric; discrete elements; smart agriculture; visual recognition; decision control; end-effector; harvesting robots; nitrogen content; water content; double hidden layer; BP neural network; particle swarm optimization; fruit picking; multiple robotic arms; end-picker; target detection; task planning; clustered lawnmowers; grid-based method; task allocation; comprehensive coverage path planning; n/a; UAV hyperspectral; leaf nitrogen content (LNC); feature optimization; 3D LiDAR; positioning; canopy length measurement