Agricultural Crop Yield Prediction Using Artificial Intelligence and Satellite Imagery
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Publication date: 2019-03-16
Eurasian J Anal Chem 2018;13(Engineering and Science SP):emEJAC181194
The influence of climate change and its unpredictability, has caused majority of the agricultural crops to be affected in terms of their production and maintenance. Forecasting or predicting the crop yield well ahead of its harvest time would assist the strategists and farmers for taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. Such predictions will also support the allied industries for strategizing the logistics of their business. Several means and approaches of predicting and demonstrating crop yields have been developed earlier with changing rate of success, as these don’t take into considerations the weather and its characteristics and are mostly empirical. For this a combined constructional and methodological approach is proposed like variety inception, pesticide & fertilizer management, integrated cropping, rainwater harvesting, efficient irrigation techniques etc. would also be needed. The neural network algorithm is less prone to error than other machine learning and data mining techniques, making it an effective machine learning tool for predicting crop yields. The ANN back propagation algorithm is used to determine the appropriate weight value to calculate the error derivative. The accuracy of the crop yield estimation for the diverse crops involved in strategizing and planning is deliberated to be one of the utmost significant issues for agronomic production purposes. The yield prediction is still considered to be a major issue that remains to be explained based on available data for some agricultural areas. Crop monitoring and forecasting of crop yields for the proposed system will be carried out via satellite images with low resolution. The combination of extensive and extended topographical coverage and its high temporal frequency make these images an appropriate choice for the prediction of crop yields. To ease the training, the dimensionality of the data is reduced by supposing that the position of pixels doesn’t influence the typical crop yield. The prototype distinguishes between crops, the infrared and temperature bands of images taken during apex growing season contribute the most to the crop prediction. Using Satellite Imagery and CNN algorithm to forecast crops in all states produces better efficiency than only using ANN algorithm. The main aim is to compare the output of ANN and CNN to verify whether the results are accurate for crop yield forecasting.