مشخصات پژوهش

Millets, classified as climate-smart Nutri-cereals, are characterized by considerable postharvest losses at a time when the global production of these cereals has been gradually escalating. This review briefs smart sensor technologies ranging from physical, chemical, and optical to biosensors interfaced effectively with machine learning algorithms, DL algorithms for automatic assessment, identification of contamination, as well as predictive forecasts, respectively. Solutions based on operational issues of millet-scaled sensor calibration, along with reduced on-farm applicability of existing lab-based models, will be described. Additionally, a step-wise growth plan for a low-cost, handheld, farmer-friendly sensor will be discussed.