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<p>Sugar-yielding crops such as sugarcane (<i>Saccharum spp.</i>), sugar beet (<i>Beta vulgaris</i>), and sweet sorghum (<i>Sorghum bicolor</i>) are vital to the global agro-industrial economy, yet their productivity is increasingly constrained by diverse biotic and abiotic stressors. This review synthesizes recent advances in artificial intelligence (AI), robotics, and the Internet of Things (IoT) that are transforming sugar crop health monitoring, disease diagnostics, and decision support. Deep-learning algorithms and convolutional neural networks (CNNs) have achieved diagnostic accuracies exceeding 95% for major diseases, while unmanned aerial vehicles (UAVs) and multispectral imaging enable high-resolution, spatiotemporal assessment of crop health. Agribots designed for autonomous scouting, precision weeding, nutrient optimization, and mechanized harvesting enhance field efficiency, reduce dependence on manual labor, and lower environmental footprints. IoT-enabled sensor arrays continuously monitor soil, canopy, and microclimatic parameters, facilitating predictive analytics for irrigation, nutrient management, and stress forecasting. Emerging paradigms—such as generative and regenerative AI, blockchain-based traceability, and 5G-edge computing—further strengthen system scalability, data integrity, and operational responsiveness. Despite challenges related to implementation cost, data interoperability, and digital literacy gaps, these convergent technologies collectively offer a transformative framework for precision-oriented, climate-resilient, and sustainable sugar agriculture. The proposed integrative Agribot–IoT–AI model establishes a closed-loop advisory system linking real-time sensing, predictive modeling, and farmer interaction, supporting sustainable intensification and bio-economic resilience.</p>

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A Critical Review of Agribots and Intelligent Systems for Sugar Crop Health and Farmer Advisory

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Sudipta Mahato,
  • Ritwik Sahoo,
  • Dipankar Barai,
  • Waghamare Minal Bhujangrao,
  • Sumit Sow,
  • Shivani Ranjan,
  • Subir Dutta,
  • Dibyendu Ghosh

摘要

Sugar-yielding crops such as sugarcane (Saccharum spp.), sugar beet (Beta vulgaris), and sweet sorghum (Sorghum bicolor) are vital to the global agro-industrial economy, yet their productivity is increasingly constrained by diverse biotic and abiotic stressors. This review synthesizes recent advances in artificial intelligence (AI), robotics, and the Internet of Things (IoT) that are transforming sugar crop health monitoring, disease diagnostics, and decision support. Deep-learning algorithms and convolutional neural networks (CNNs) have achieved diagnostic accuracies exceeding 95% for major diseases, while unmanned aerial vehicles (UAVs) and multispectral imaging enable high-resolution, spatiotemporal assessment of crop health. Agribots designed for autonomous scouting, precision weeding, nutrient optimization, and mechanized harvesting enhance field efficiency, reduce dependence on manual labor, and lower environmental footprints. IoT-enabled sensor arrays continuously monitor soil, canopy, and microclimatic parameters, facilitating predictive analytics for irrigation, nutrient management, and stress forecasting. Emerging paradigms—such as generative and regenerative AI, blockchain-based traceability, and 5G-edge computing—further strengthen system scalability, data integrity, and operational responsiveness. Despite challenges related to implementation cost, data interoperability, and digital literacy gaps, these convergent technologies collectively offer a transformative framework for precision-oriented, climate-resilient, and sustainable sugar agriculture. The proposed integrative Agribot–IoT–AI model establishes a closed-loop advisory system linking real-time sensing, predictive modeling, and farmer interaction, supporting sustainable intensification and bio-economic resilience.