Research status and applications of nature-inspired algorithms for agri-food production

Authors: Huang, Yanbo

Submitted to: International Journal of Agricultural and Biological Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/16/2020
Publication Date: 3/30/2020
Citation: Huang, Y. 2020. Research status and applications of nature-inspired algorithms for agri-food production. International Journal of Agricultural and Biological Engineering. 13(4):1-9. https://doi.org/10.25165/j.ijabe.20201304.5501.
DOI: https://doi.org/10.25165/j.ijabe.20201304.5501

Interpretive Summary: Algorithms which are naturally inspired are developed more and more and applied to solve problems in agricultural and food systems with improved performance compared to conventional algorithms. A scientist in USDA-ARS Crop Production Systems Research Unit at Stoneville MS has been investigating various such algorithms from machine learning and bionic computing for applications in agricultural and food production. This paper presents and discusses the development of artificial neural networks, deep learning, and bionic algorithms in conjunction with their biological connections for agri-food applications. The results of my independent studies are summarized and presented, which indicated that the potential of the algorithms is great in crop production and food processing.

Technical Abstract: Nature-inspired algorithms have been developed with biological mimicking. Machine learning algorithms from artificial neurons and artificial neural networks have been developed to mimic the human brain with synthetic neurons. This research can be traced back to the 1940s and has been expanded to agri-food problem solving in the last three decades. Now, the research and applications have entered the stage of deep learning with more layers and neurons that have complex connections to extract deep features of the target. In this paper, the development of artificial neural networks and deep learning algorithms is presented and discussed in conjunction with their biological connections for agri-food applications. My independent studies previously conducted are summarized with newly conducted being presented. At the same time, the algorithms motivated from recent bionics studies are be compared and discussed for their potentials for agri-food production.