Nazir N¹*, Khalil A¹, Rashid M² Asif M³, Pandith A¹, Malik RA¹, Gulzar U¹, Baghat Sakshi ¹ and Kumar Amit¹

¹Division of Fruit Science, Faculty of Horticulture, Sher-e-Kashmir University of Agricultural Sciences and Technology Shalimar, Srinagar, Jammu and Kashmir India
²Division of Basic Science and humanities Faculty of Agriculture Sher-e-Kashmir University of Agricultural Sciences and Technology Shalimar, Srinagar, Jammu and Kashmir, India
³Division of Silviculture and Agroforestry, Faculty of Forestry, Sher-e-Kashmir University of Agricultural Sciences and Technology Shalimar, Srinagar, Jammu and Kashmir, India

*Corresponding Author: Nazir N, Division of Fruit Science, Faculty of Horticulture, Sher-e-Kashmir University of Agricultural Sciences and Technology Shalimar, Srinagar, Jammu and Kashmir India.

Received: May 31, 2024; Published: June 27, 2024

Abstract

Horticulture is an essential addition to the growth in the economy of any nation. In light of increasing populations, fluctuating environmental variables, and finite resources, meeting the dietary needs of the current populace has become an increasingly difficult endeavour. Horticulture has recently transitioned from an input-intensive to a knowledge-intensive sector due to the fact that vast quantities of information related to Horticulture can be preserved, shared, and examined to generate insights. An in-depth review of advancements in the swiftly progressing domain of deep learning is offered. Cognitive Horticulture, also referred to as precision Horticulture, has surfaced as a novel approach to tackle modern hurdles in Horticulture sustainability. Deep Learning is the process by which this cutting-edge technology operates. It imparts learning capability to the machine that doesn't need explicit programming. A key component of the impending Horticulture revolution is DL and Connectivity of Things (IoT)-enabled Horticulture gear. It is suggested that an article be written that offers a thorough analysis of machine learning's applications in Horticulture. The primary areas of research are agricultural yield prediction, disease and weed detection in crops, species identification, and prediction of soil parameters including moisture content and biological matter. This article provides a thorough analysis of the literature on the use of machine learning to horticultural production systems.

 Keywords: Horticulture; Economic Growth; Sustainability; ML Applications; Deep Learning; Edge Technology; Data Analytics

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Citation

Citation: Nazir N., et al. “Envisioning the Future of Intelligent Horticulture: A Theoretical Exploration of Deep Learning's Transformative Potential". Acta Scientific Veterinary Sciences 6.7 (2024): 36-44.

Copyright

Copyright: © 2024 Nazir N., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.