Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

• Published in: Agronomy (Basel) Vol. 12; no. 3; p. 748
• Main Authors: Cravero, Ania, Pardo, Sebastian, Sepúlveda, Samuel, Muñoz, Lilia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2022
• Subjects: Agricultural equipment; Agricultural production; Agriculture; Algorithms; Animal husbandry; Animal welfare; Artificial intelligence; Big Data; challenges; Cost control; Crop management; Crops; Data analysis; Data processing; Decision making; Design modifications; Disease detection; Farmers; Farms; Food; Food supply; Growth models; Learning algorithms; Literature reviews; Livestock; Livestock production; Machine learning; Neural networks; Soil management; Soil water; systematic literature review; Systematic review; Water management
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12030748

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers’ decision making, water management, soil management, crop management, and livestock management. Crop management includes yield prediction, disease detection, weed detection, crop quality, and species recognition. On the other hand, livestock management considers animal welfare and livestock production. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in agricultural Big Data. We conducted a systematic literature review applying the PRISMA protocol. This review includes 30 papers published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used. A significant challenge is the design of agricultural Big Data architectures due to the need to modify the set of technologies adapting the machine learning techniques as the volume of data increases.