Reprint

Agricultural Environment and Intelligent Plant Protection Equipment

Edited by
July 2024
256 pages
  • ISBN978-3-7258-1583-8 (Hardback)
  • ISBN978-3-7258-1584-5 (PDF)

This is a Reprint of the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment that was published in

Biology & Life Sciences
Chemistry & Materials Science
Environmental & Earth Sciences
Summary

The agricultural environment refers to the combination of various natural and artificially modified natural factors that affect the survival and development of agricultural organisms, including farmland, forest, grassland, irrigation water, air, light, the heat and chemical fertilizers applied to farmland, pesticides, and agricultural equipment. These factors constitute a comprehensive agricultural environment system, interacting with each other and affecting agricultural production together.

As a vital component of the agricultural environment, plant protection equipment plays an indispensable role in agricultural production. The intellectualization of plant protection equipment is important in driving agricultural development processes. With the advantages of saving time and effort, precision, and a high efficiency, intelligent plant protection equipment makes great contributions to cost reduction, increasing incomes, as well as to the healthy and sustainable development of agriculture.

In this Special Issue, we aim to exchange knowledge on any aspect related to the agricultural environment and intelligent plant protection equipment to promote sustainable agricultural development.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
air-assisted sprayer; airflow distribution pattern; deposit coverage; spray penetration; farmland information; disease and pest identification; temperature and humidity acquisition; mobile monitoring; on-line pesticide mixing; pipeline spraying; pesticide mixing apparatus; CFD; orthogonal experiment; optimization; orchard; sprayer; pesticide application; canopy; deposition rate; hyperspectral imaging technology; plant disease identification; photo response; machine learning; multi-scale feature fusion; U-Net; UAV; multi-spectral image; complex wheat fields; ground object classification; agroecosystem; revegetation; soil erosion; deposition; enzyme activity; microbial metabolic limitation; conventional orchard; spraying trailer; navigation; path tracking; reinforcement learning; spraying–dosing robot group; collaborative navigation; map construction; traditional orchard; droplet size; deposition rate; downwash airflow; flat fan nozzle; redgram leaf; UAV sprayer; aerial application; drift control; agricultural aircraft; pesticide application; unmanned aerial spraying system (UASS); spray drift; test bench; petri dish; droplet size; wind speed; nozzle; cumulative drift percentage; correlation analysis; agrivoltaics; agricultural building; photothermal environment; ECOTECT; simulation; smart mango orchard; co-operative plant protection; UAV; pest control; n/a