Badgujar, C.; Das, S.; Figueroa, D.M.; Flippo, D. Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review. Agriculture2023, 13, 357.
Badgujar, C.; Das, S.; Figueroa, D.M.; Flippo, D. Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review. Agriculture 2023, 13, 357.
Badgujar, C.; Das, S.; Figueroa, D.M.; Flippo, D. Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review. Agriculture2023, 13, 357.
Badgujar, C.; Das, S.; Figueroa, D.M.; Flippo, D. Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review. Agriculture 2023, 13, 357.
Abstract
Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.
Keywords
Tillage; Traction; Compaction; Neural networks; Support vector regression
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
