DEVELOPMENT AND CHALLENGES IN INTELLIGENT PREDICTION OF CROP GROWTH: A SYSTEMATIC LITERATURE REVIEW

ABSTRACT

Agricultural research is growing. Specifically, crop forecasting is greatly influenced by earth and climate factors, including temperature, humidity, and rainfall. One of the problems in agriculture is that of crop growth prediction. This work provides a methodical review of the approaches to predicting the growth rate of crops. It reviews problems in growth prediction and the results achieved in solving the various problems in light of the strategy employed. These include machine learning, hybrid techniques, ensemble techniques, and other techniques. These techniques distinctively mark this study from previous reviews in the subject matter of crop growth prediction. However, most of the techniques could not tackle the problems in crop growth prediction owing to their limitations. The goal of this paper is to help both experienced and inexperienced researchers identify specific challenges with crop growth prediction so that further advancements can be made at resolving them.

Keywords: Climate, prediction techniques, Growth Rate, Machine learning, Forecasting, Growth tracking