Satellite Imaging for Crop Yield Forecasting Using AI

Abstract

Accurate crop yield forecasting plays a crucial role in ensuring food security, supporting effective agricultural planning, and strengthening the rural economy. Traditional yield estimation methods, which often depend on manual field surveys and conventional statistical techniques, may not provide the level of accuracy, scalability, and timeliness required for modern agricultural systems. These approaches are often constrained by the difficulty of collecting large-scale ground-level data and their limited ability to adapt to changing weather conditions and climate variability.This work proposes an Artificial Intelligence (AI)- based crop yield forecasting framework that utilizes satellite imagery and advanced deep-learning techniques, including Convolutional Neural Networks (CNNs) and transformer-based models. The proposed framework processes large volumes of agricultural and remote-sensing data to identify important patterns associated with crop growth and productivity. By utilizing multispectral and hyperspectral satellite imagery, the system can analyze vegetation characteristics, soil conditions, moisture levels, and other environmental factors that influence crop development and final yield. The integration of AI-driven satellite image analysis with predictive analytics enables more accurate and timely crop yield estimation across large agricultural regions. The proposed approach can assist farmers, agricultural experts, and policymakers in making data-driven decisions related to crop management, resource allocation, irrigation planning, and food-production strategies. By providing near real-time insights into crop and field conditions, the framework contributes to improved agricultural productivity, enhanced climate resilience, and the development of a more efficient and sustainable food-production ecosystem.