Spatial Data-Based Prediction Models for Crop Yield Analysis: A Systematic Review

Agriculture plays a vital role in the global economy. WHO states that there are three pillars of food security: availability, access, and usage. Among these three pillars, availability is the most important one. Ensuring food for the entire population of a country is achieved only through an increase in crop production. Accurate and timely forecasting of the weather can help to increase the yield production. Early prediction of crop yield has a vital role in food availability measure. Researchers monitor different parameters that affect the crop yield regularly. Yield prediction did through either statistical data or spatial data. Crop monitoring through remote sensing can cover a vast land area. Therefore, spatial data-based prediction is widespread in recent decades. Satellite images such as multispectral, hyperspectral, and radar images were used to calculate crop area, soil moisture, field greenness, etc. Among these imaging modalities, hyperspectral images give more accurate results, but its higher dimensionality is a challenging issue. Optimal band selection from hyperspectral images helps to reduce this curse of dimensionality problem. Crop area is one of the essential parameters for yield prediction. The exact crop area measure can be achieved only through the best crop discrimination methods. This paper provides a comprehensive review of crop yield prediction using hyperspectral images. Besides, we explore the research challenges and open issues in this area.