SMART FARM: Crop, Fertiliser and Disease Management through Machine Learning and Deep Learning Applications

MuttareddyBhavika; Pannala Ashwanth; Valaboju Abhishek1

1

Publication Date: 2024/07/29

Abstract: In the environment of global challenges similar as population growth, climate change, and resource constraints, the agrarian sector faces significant pressure to enhance productivity and sustainability. This paper explores the conception and perpetration of a Smart Farm, which leverages advanced technologies similar as the Internet of effects( IoT), artificial intelligence( AI), big data analytics, and robotics to optimize husbandry practices. The Smart husbandry, automated ministry, and data driven decision-making processes to increase crop yields, reduce resource consumption, and ameliorate environmental stewardship. Case studies punctuate successful operations of these technologies in colorful husbandry surrounds, demonstrating significant advancements in effectiveness and sustainability. The findings emphasize the eventuality of Smart granges to transfigure traditional husbandry into a largely productive, flexible, and sustainable assiduity, able of meeting unborn food security demands.

Keywords: No Keywords Available

DOI: https://doi.org/10.38124/ijisrt/IJISRT24JUL549

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JUL549.pdf

REFERENCES

  1. Hangzhi Guo, Alexander Woodruff, Amulya Yadav (2020) - Improving Lives of Indebted Farmers Using Deep https://ojs.aaai.org/index.php/AAAI/artic le/view /7039
  2. Bai, S.; Kolter, J. Z.; and Koltun, V. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. ArXiv preprint arXiv:1803.01271.
  3. Barik, N. 2018. Analysis ofinterventions addressing farmer distress in Rajasthan. https://www. copenhagenconsensus.com/sites/default/files/rajfarmer distress sm.pdf
  4. DARD. 2019. Vegetable Production in Kwazulu-Natal: Length of Growing Period. https://www. kzndard.gov.za/images/ Documents/Horticulture/Veg prod/length of growing period.pdf.
  5. Ma, W.; Nowocin, K.; Marathe, N.; and Chen, G. H. 2019. An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbors. In Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, 6. ACM.
  6. NCRB. 2019. National Crime Records Bureau. http://ncrb.gov. in/.
  7. Tambe, M., and Rice, E. 2018. Artificial Intelligence and Social Work. Artificial Intelligence for Social Good. Cambridge UniversityPress