Contemporary machine learning applications in agriculture: Quo Vadis?. (18th March 2022)

• Record Type: Journal Article
• Authors: Mahmood, Atif; Tiwari, Amod Kumar; Singh, Sanjay Kumar; Udmale, Sandeep S.
• Is Part Of: Concurrency and computation. Volume 34:Number 15(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-18
• Subjects: agriculture -- classification -- deep learning -- machine learning -- recognition; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6940
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Agricultural automation is an emerging subject today to accomplish the food demands of individuals across the globe. Machine learning is one such agricultural automation tool that has been adopted briskly in the recent decade due to its ability to process countless input data and handle non‐linear tasks. Availability and continuous development of agricultural data led the machine learning pervasive in multiple aspects of agriculture. This paper systematically analyses and summarizes the 81 quality research efforts published in the past decade dedicated to the various contemporary machine learning applications in agriculture and food production systems. We examined and categorized each agricultural problem under study into four categories and each category into its subcategories. The finding demonstrates the contemporary applications of machine learning in broad agricultural subcategories and determines where it is heading shortly; based upon contributions of researchers, utilization of machine learning models/algorithms, and the availability of agricultural datasets. Through the analysis, it is discovered that the current innovation can help the improvement of agricultural automation to accomplish the advantages of minimal cost, high efficiency, and better precision. This paper can serve as an investigatory guide for researchers, academicians, engineers, and manufacturers to understand and apply modern and upgraded cognitive technologies to each subcategory of the agricultural sector.