Precision agriculture with AI-based responsive monitoring algorithm

被引:2
|
作者
Dusadeerungsikul, Puwadol Oak [1 ]
Nof, Shimon Y. [2 ,3 ]
机构
[1] Chulalongkorn Univ, Fac Engn, Dept Ind Engn, Bangkok, Thailand
[2] Purdue Univ, PRISM Ctr, W Lafayette, IN USA
[3] Purdue Univ, Sch Ind Engn, W Lafayette, IN USA
关键词
Collaborative control; Error prevention; Artificial intelligent; Optimization; COLLABORATIVE CONTROL PROTOCOL; PLANT-DISEASE DETECTION; SYSTEMS; POLICY; MODEL;
D O I
10.1016/j.ijpe.2024.109204
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Precision Agriculture (PA) is a relatively new farming approach, applying science and technology to enhance cost-effectiveness and improve food security by optimizing agricultural practices through the treatment of each crop individually. To support the new practice, an AI-based, responsive monitoring algorithm, called the Dynamic-Adaptive Search algorithm, has been developed to minimize operation costs with the benefit of acquiring new and timely information. Three modules of the algorithm are 1) Module for image processing based on AI, 2) Module for error-responsive search expansion, and 3) Module for estimating stress propagation. Computational experiments have demonstrated that the newly developed algorithm outperforms other alternatives, yielding significantly higher system performance and system gain, compared to other algorithms. The sensitivity analysis confirms the algorithm's ability to deliver within +/- 10% of the theoretical optimal value, resulting in economic benefits under varying conditions. The algorithm's applications can be extended to other decision-making situations involving cost-benefit tradeoffs of acquiring more data.
引用
收藏
页数:15
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