A Geospatial Based Crop Yield Estimation: A Case Study of Dindigul District

Agriculture is a cornerstone of global economies and livelihoods, with precise crop production estimates and yield predictions being critical for farmers, agriculturalists, and policymakers. Such estimates offer valuable insights for informed decision-making, especially in the context of the current climate crisis, which has significantly impacted key crops like rice and maize. In Dindigul district, official reports reveal a steady decline in rice production. To analyze the spatial variation in rice yields, spatial data is crucial, and remote sensing proves indispensable for managing crops at a site-specific level by providing both spatial and temporal insights. The integration of remote sensing and GIS technologies in crop production estimation has garnered widespread attention for its potential to enhance accuracy and efficiency in agricultural practices. Crop growth models, which simulate the interactions between plants and their environment, enable predictions of crop yields and assessments of climate change impacts on food security. This paper explores the application of remote sensing data to estimate rice yields in Dindigul district. Satellite imagery with a 30-meter spatial resolution, acquired from NASA’s Landsat-8 program during the mature stage of the Kharif season (October), was processed using GIS platforms. Machine learning techniques were employed to classify land use and land cover, while a JavaScript algorithm in the Google Earth Engine platform was utilized to compute the Normalized Difference Vegetation Index (NDVI), which was correlated with historical rice yield data. A principal component regression model was then developed to predict rice yields based on NDVI values from the study area. The model's predictions were compared with official agricultural statistics from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), demonstrating an 80% accuracy rate in estimating rice yields. Furthermore, the study examined long term changes in rainfall and temperature over the past 70 years, revealing an increase in both factors, which has notably influenced local cropping patterns.