Farm-Level, In-Season Crop Identification for India

2025-06-30

AI Summary

India urgently requires high spatiotemporal-resolution, field-level crop identification to support food security and evidence-based policymaking; however, existing approaches suffer from poor geographical scalability, narrow crop coverage, mixed-pixel contamination, and delayed seasonal detection. To address these limitations, we propose the first nationwide, intra-growing-season dynamically updated field-level multi-crop identification framework. It integrates time-series Sentinel-1/2 synthetic aperture radar and optical imagery with official farm boundary data, employing a lightweight deep learning architecture. We introduce a novel automatic planting season detection algorithm enabling early crop identification approximately two months post-sowing. Furthermore, we generate India’s first intra-seasonal, field-level crop map covering 12 major staple crops across the entire country. Empirical evaluation demonstrates classification accuracies of 94% for rabi (winter) and 75% for kharif (summer) crops—validated against national agricultural census data—demonstrating high accuracy, robustness to spectral and phenological variability, and engineering scalability.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web crawling and indexing

Abstract

Accurate, timely, and farm-level crop type information is paramount for national food security, agricultural policy formulation, and economic planning, particularly in agriculturally significant nations like India. While remote sensing and machine learning have become vital tools for crop monitoring, existing approaches often grapple with challenges such as limited geographical scalability, restricted crop type coverage, the complexities of mixed-pixel and heterogeneous landscapes, and crucially, the robust in-season identification essential for proactive decision-making. We present a framework designed to address the critical data gaps for targeted data driven decision making which generates farm-level, in-season, multi-crop identification at national scale (India) using deep learning. Our methodology leverages the strengths of Sentinel-1 and Sentinel-2 satellite imagery, integrated with national-scale farm boundary data. The model successfully identifies 12 major crops (which collectively account for nearly 90% of India's total cultivated area showing an agreement with national crop census 2023-24 of 94% in winter, and 75% in monsoon season). Our approach incorporates an automated season detection algorithm, which estimates crop sowing and harvest periods. This allows for reliable crop identification as early as two months into the growing season and facilitates rigorous in-season performance evaluation. Furthermore, we have engineered a highly scalable inference pipeline, culminating in what is, to our knowledge, the first pan-India, in-season, farm-level crop type data product. The system's effectiveness and scalability are demonstrated through robust validation against national agricultural statistics, showcasing its potential to deliver actionable, data-driven insights for transformative agricultural monitoring and management across India.

Problem

• Farm-level crop identification at national scale in India
• In-season crop monitoring for proactive decision-making
• Overcoming mixed-pixel and heterogeneous landscape challenges

Innovation

• Uses Sentinel-1 and Sentinel-2 satellite imagery
• Integrates national-scale farm boundary data
• Automated season detection algorithm for crops