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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 14 | Issue 3 | Month- March 2026
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Published Paper Details:
Paper Title
A Study On Using Deep Learning To Predict Plant Growth And Yield In Greenhouse Environment
Authors
C. Chinmai Shree, Mamidi Tarani
Keywords
Plant growth prediction, CNN, Deep CNN, Data Set, Environmental sensor data, Agricultural productivity, Deployment.
Abstract
This study demonstrates the potential of deep learning models to significantly enhance the prediction of plant growth and yield in greenhouse environments. By integrating image analysis and environmental sensor data, our approach provides accurate, timely predictions that can optimize greenhouse operations. The success in predicting key growth metrics and yield outcomes underscores the utility of deep learning in precision agriculture. This predictive capability supports more informed decisions in irrigation, nutrient management, and pest control, contributing to increased efficiency and sustainability. Future research will aim to broaden the dataset, refine model accuracy, and explore application across diverse crop types and growing conditions. The promising results affirm the transformative impact of deep learning in modern agriculture
IJCRT's Publication Details
Unique Identification Number - IJCRT2407956
Paper ID - 266718
Page Number(s) - i545-i555
Pubished in - Volume 12 | Issue 7 | July 2024
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
C. Chinmai Shree, Mamidi Tarani, "A Study On Using Deep Learning To Predict Plant Growth And Yield In Greenhouse Environment", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 7, pp.i545-i555, July 2024, Available at :http://www.ijcrt.org/papers/IJCRT2407956.pdf
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Volume 14 | Issue 3
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March 2026
Volume 14 | Issue 3
Last Date :
31-Mar-2026
Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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