Harnessing AI and Remote Sensing for Precision Sugarcane Farming: Tackling Water Stress, Salinity, and Nitrogen Challenges

. Vinayaka^1*Rama Chandra Prasad Pillutla²G. Avinash³. Amaresh⁴R. Arun Kumar⁵P. Murali¹C. Palaniswami⁶P. Govindaraj⁴

• ¹Statistics and Economics Section, ICAR-Sugarcane Breeding Institute, Tamil Nadu-641007, Coimbatore, India
• ²Lab for Spatial Informatics, International Institute of Information Technology, Gachibowli, Telangana-500032, Hyderabad, India
• ³Executive Chairman & CEO, Avyagraha Research and Analytics LLP, Ramasagara, Karnataka-583132, Ballari, India
• ⁴Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Tamil Nadu-641007, Coimbatore, India
• ⁵Plant Physiology Section, ICAR-Sugarcane Breeding Institute, Tamil Nadu-641007, Coimbatore, India
• ⁶Soil Science Section, ICAR-Sugarcane Breeding Institute, Tamil Nadu-641007, Coimbatore, India

Sugarcane is a vital cash crop with substantial significance in both global sugar production and the biofuel industry. However, its sustainable cultivation faces persistent challenges from environmental stressors, particularly salinity and water scarcity. In recent years, the integration of artificial intelligence (AI) and remote sensing (RS) technologies has proven to be a transformative approach for detecting and evaluating these stress conditions, offering critical insights for advancing precision agriculture (PA). This review explores the utilization of satellite imagery and sensor-based data including RGB, multispectral, hyperspectral imaging, and unmanned aerial vehicles (UAVs) to monitor stress-related parameters in sugarcane farming. It emphasizes key indices used to assess water stress, generate salinity stress maps, and estimate nitrogen levels, demonstrating their role in equipping farmers with actionable information to optimize irrigation and nutrient management strategies. These innovations significantly enhance crop productivity while promoting environmental sustainability. The review sets out three core objectives: (i) to evaluate the contribution of AI and RS in assessing water stress in sugarcane cultivation, (ii) to examine methods for mapping salinity stress using RS and AI tools, and (iii) to highlight the relevance of spectral indices in tracking nitrogen status in sugarcane crops. Drawing upon reputable bibliographic sources such as Google Scholar, Scopus, ResearchGate, and Web of Science, along with current literature on AI and RS applications in sugarcane stress assessment, the review consolidates detailed information on advanced sensors and UAV technologies. It also introduces novel deep learning models and sensor platforms that have received limited attention in prior studies. In conclusion, the review affirms that AI-driven remote sensing is a highly effective approach for monitoring and managing critical stress factors in sugarcane production. It not only contributes to enhanced yield and crop quality but also delivers significant socio-economic and environmental benefits, marking a major step forward in achieving sustainable and efficient sugarcane cultivation.