AI-Enabled Crop Recommendation System Based on Soil and Weather Patterns

AI-Enabled Crop Recommendation System Based on Soil and Weather Patterns

DOI: 10.4018/978-1-6684-8516-3.ch010
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Agriculture is the foremost factor which is important for the survival of human beings. Farming contributes to a very big part of GDP; still, several areas exist where improvements are required. One of those is crop recommendation. Crop productivity is boosted as a result of accurate crop prediction. As crop production has already started to suffer from climate change, improving crop output is consequently desirable because agronomists are impotent to select the appropriate crop(s) depending on environmental and soil parameters, and the mechanism of forecasting the selection of the appropriate crops manually has failed. Factors like soil characteristics, soil types, climate characteristics, temperature, rainfall, area, humidity, geographic location etc. affect crop forecast. This chapter focuses mainly on building a recommendation system, i.e., suggesting the kind of the crop by applying various machine learning and deep learning techniques depending upon several parameters. The system would help the farmers for the appropriate decision to be taken regarding the crop type.
Chapter Preview
Top

Literature Review

(Van et al., 2020) conducted a systematic literature assessment. The analysis shows that soil type, temperature and rainfall are the most frequently utilized features, while ANN`s are the most frequently used algorithm in this model. (Rashid et al., 2021) reviewed various ML algorithms for predicting the agricultural yield with extra special importance on palm oil yields. (Kalimuthu et al., 2020) have used an approach where Naive Bayes algorithm is used. (Sharma et al., 2021) have given a comprehensive evaluation of ML applications in the realm of agriculture. By identifying and diagnosing eating disorders, reproductive patterns, or behaviour prediction, machine learning with computer vision can be utilised to increase livestock productivity.

Complete Chapter List

Search this Book:
Reset