Forecasting agricultural crop yield variations using big data and supervised machine learning

The government of Sri Lanka is struggling to make appropriate policy decisions regarding paddy cultivation due to absence of accurate and timely data to estimate the paddy yield, land usage for paddy cultivation and area affected by various paddy diseases. Remote sensing data based machine learning implementations can be identified as a potential solution for the above issue, as remote sensing data can be used for accurate and timely estimations. However, the traditional remote sensing data resources have failed to generate accurate estimates regarding cultivated paddy extent estimations. In this study, novel optical remote sensing data resources and a hybrid approach are employed to mitigate previously reported issues. Furthermore, a multi-temporal approach is used instead of traditional mono-temporal approach by leveraging deep neural networks. This study also consists of a comprehensive comparison on novel optical remote sensing data resources and the evaluations of the capability of using deep neural networks for temporal remote sensing analysis. Outcomes of the study shows quite impressive results over 97% of accuracy in terms of cultivated paddy area detection using optical remote sensing imagery. Moreover, the research was extended to identify cultivated paddy areas using synthetic aperture radar (SAR) imagery. It also outputs a promising result over 96% of accuracy in terms of detecting cultivated paddy regions. The study then extends to detect Brown Planthopper attacks in cultivated paddy fields. Brown Planthopper is considered as the most destructive insect in paddy cultivation. There are no previous studies for identifying Brown Planthopper attacks using satellite remote sensing data under field conditions. In this study, ratio and standard difference indices derived from optical imagery are fed into a Support Vector Machine model to identify the regions affected by Brown Planthopper attacks. Using the results of cultivated paddy fields detection model as a filter, SVM model results are improved. The combined approach shows accuracy over 96% for detecting Brown Planthopper attacks