Paddy fields detection on Sentinel-2 satellite images using EfficientDet model

Agricultural monitoring plays an important role in ensuring food security and sustainable farming practices. This project focuses on the task of paddy field detection on the satellite images collected from the agricultural lands of Andhra Pradesh. Using satellite images of Sentinel-2 and deep learning techniques, our approach aims to improve the accuracy and efficiency of paddy land identification. The project employs a deep learning model, which is the EfficientDet trained on a carefully annotated dataset, to detect the paddy fields in the region. The utilization of remote sensing technology allows for scalable and timely monitoring across vast agricultural lands. The selected model architecture, combined with fine-tuning strategies, ensures adaptability to the unique spatial and seasonal characteristics of South Indian agriculture. Results from the project showcase the capability of the proposed approach in accurately identifying and detecting paddy crops. The integration of advanced technologies for precision agriculture contributes to informed decision-making, resource optimization, and overall sustainability in the farming sector. To collect the ground truth data, we used the AP GIS portal, which is supervised by the Andhra Pradesh agriculture department, which has the data of the percentage of paddy lands in small villages. The places with more than 95 percent of paddy lands are selected for better data samples. The collected samples are cropped and labelled, and trained on the model architecture and verified for accuracy. This project not only advances the field of agricultural monitoring but also holds significant importance for crop management and supporting the livelihoods of farmers of our state. The proposed model achieved an accuracy of 86%, demonstrating its reliability in detecting paddy fields from Sentinel-2 satellite imagery.