Abstract

Horticulture is a versatile field which encompasses a plethora of day to day strategic decisions like varietal selection, optimisation of resources, understanding the mechanisms of the phenology, identification of plant invaders both in the micro and macro level, wise and judicious use of plant protectants, yield prediction & assessment, post harvesting & handling, strategic way of understanding the pulse of consumer’s popular demands and efficient way of marketing. Fruit trees are perennial unlike annual vegetable and cereal crops where there is a high prerequisite for efficient modelling of canopy architecture, photosynthesis, nutrient uptake, pest forecasting etc where the ill-effects of climate change are bringing out huge losses in the existing germplasm, annual turnover of the farmers and emergence of unheard pests and diseases. An invincible foresight or preparedness against such vagaries can be brought out by efficient modelling mechanisms combining the physiology, phenology and vital requirements of fruit trees with the interacting ecosystem of the land where it is present. Extrapolating such models from the local level to a general situations always gives fruitful results and it further aids in strengthening the present protocols. With the advancement of machine learning and deep learning in precision agriculture, problems of farmers and orchardists are being solved at a faster pace with the help of sensors in identification of problems and its alleviation using fast and error-free processing at pre-harvesting, harvesting and post-harvesting stages of fruit crops. In fact it is also one of the major concerns among people regarding the complete replacement of human power in the crucial decision support systems for agriculture and farming.

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