Geographical Remote Data Analysis for Agriculture Crop Production Prediction

Author(s)	Mr. Pavan Kumar Patel, Prof. Arjun Rajput
Country	India
Abstract	Accurate prediction of crop water requirements is vital for improving agricultural productivity and achieving food security. Combining time-series remote sensing data with deep learning models enables precise, real-time water demand estimation. Remote sensing offers continuous monitoring of crop and environmental conditions across large areas. Data mining algorithms analyze these patterns to optimize irrigation and resource management decisions. In this work geographical data in form of SPI, VCI images were taken for identifying the environmental condition. This paper has extract the values from images as per scales of image in pre-processing step. Paper has further classify the data in training and non-training region by use of wolf genetic algorithm. Trained data was use for the learning of machine and it was found that proposed model has increases the work efficiency. Trained model proposed the amount of water requirement in real environment data. Results shows that proposed GWWRP (Grey wolf based Water Requirement Prediction) model has improved the work prediction accuracy.
Keywords	Crop Yield Prediction, Machine Learning, Data Mining, Feature Extraction, Image Processing.
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-22
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58521

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