Crop yield prediction using machine learning techniques. (January 2023)

• Record Type: Journal Article
• Authors: Iniyan, S; Akhil Varma, V; Teja Naidu, Ch
• Is Part Of: Advances in engineering software. Volume 175(2023)
• Publication Date: 2023-01
• Subjects: Machine learning -- Lasso regression -- Decision tree -- Elastic net -- Linear regression -- Exploratory data analysis -- Ridge regression -- Partial least square regression -- Gradient boosting regression -- Long short-term memory; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103326
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Machine learning is a successful dynamic device for foreseeing crop yields, just as for choosing which harvests to plant and what to do about them during the developing season. Since it operates with a large amount of data produced by several variables, the farming system is highly complicated. Methods of machine learning can aid intelligent system decision-making. The following paper investigates a variety of methods for predicting crop yields using a variety of soil and environmental variables. The main purpose of this project is to make a machine learning model make predictions. Abstract: Machine Learning is a successful dynamic device for foreseeing crop yields, just as for choosing which harvests to plant and what to do about them during the developing season. Since it operates with a large amount of data produced by several variables, the farming system is highly complicated. Methods of machine learning can aid intelligent system decision-making. The following paper investigates a variety of methods for predicting crop yields using a variety of soil and environmental variables. The main purpose of this project is to make a machine learning model make predictions. By taking into account several variables, machine learning algorithms can help farmers decide which crop to grow in addition to increasing yield. Farmers can benefit from yield estimation because it allows them to minimize crop loss and obtain the best prices for their crops. A machine learning model may be descriptive or predictive, depending on the research question and study objectives.