Review of Crop Yield Estimation using Machine Learning and Deep Learning Techniques

Abstract


 
 
 The agriculture sector is subjected to constant challenge of yield deficit due to rising population, improper resource management and shrinking agricultural land. Advance yield estimates help in systematic planning to reduce such losses. However, prediction of accurate estimates is still an open challenge due to geographical diversity, crop diversity and crop area. Recently non-destructive approach has gained attention due to its robustness and provides easy availability of data from heterogeneous resources compared to its counterpart; destructive approach which is computational, resource intensive and hence less utilized. This paper conducts a detailed study on utilization of non-destructive approach to estimate yield taking into account, input feature, and methodology. We consider five major observations namely, data acquisition, pre-processing techniques, features, methodology, and result. Moreover, we summarize analysis of each observation, extract most prominent technique, the adopted methods, and finally recommends integration of different models that can be explored to improve accuracy.
 
 

Scalable Data Processing Platform for Earth Observation Data Repositories

“…Many research projects utilize the EO satellite data for environmental monitoring and assessment, such as creating an analytical platform for weather data visualization [28] or crop yield estimation [29] using satellite image processing. Nevertheless, in the research works, the processing of EO data is carried out without considering the data processing performance considering novel data formats, solutions, and scalability.…”