AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review

Sugarcane holds a critical position in global agriculture, serving as a basis for the sugar and bioenergy sectors. The integration of remote sensing technologies and sophisticated machine learning approaches and related models has revolutionized sugarcane research. These tools offer efficient, noninvasive, and large-scale assessment methods. This review highlights the utilization of satellite imagery and sensor data, encompassing RGB, multispectral, hyperspectral, and unmanned aerial vehicles (UAVs) in sugarcane agriculture. It addresses crop identification, pest and disease management, yield and acreage estimation, modeling, phenotypic measurement, and their impact on empowering farmers with insights for optimal irrigation, fertilizer application, and overall crop management. These advancements significantly increase productivity and foster environmental sustainability. The review had dual aims: (1) consolidate RS data applications in India’s sugarcane research and development, and (2) examine the pros and cons of RS and AI methods in sugarcane farming. The review employed prominent bibliographic databases—google scholar, scopus, researchgate, and web of science—along with pertinent research articles on RS and AI applications in sugarcane, and comprehensive data on sensors and UAVs retrieved from these databases. The study concludes that AI-driven crop RS stands as an effective method for monitoring and managing sugarcane, contributing significantly to improving yield and quality, while simultaneously offering substantial benefits in social, economic, and environmental realms. However, challenges in the sugar industry, such as adapting technology, high initial costs, climate impact, communication, policy, and regulation, must be addressed.