A Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth

Author:

Karanam Santoshachandra RaoORCID,Pradeep Kumar A. B.ORCID,Yandrapati Prakash babuORCID,Tangudu NareshORCID,Peddada NagamaniORCID,Bollipelly PruthviRaj GoudORCID

Publisher

Springer Nature Switzerland

Reference45 articles.

1. Hossain, M.S., Al-Hammadi, M., Muhammad, G.: Automatic fruit classification using deep learning or industrial applications. IEEE Trans. Ind. Inf. 15, 1027–1034 (2019)

2. Ranjan, R., Chandel, A.K., Khot, L.R., et al.: Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Inform. Process. Agric. 6, 502–514 (2019)

3. Gutie´rrez, S., Wendel, A., Underwood, J.: Ground based hyperspectral imaging for extensive mango yield estimation. Comput. Electron. Agric. 157, 126–135 (2019)

4. JiJingChun Yuan, Z., Xiaojuan, Z., et al.: Application progress of unmanned aerial vehicle remote sensing in farmland information monitoring. J. Soil 56, 773–784 (2019)

5. Yao, H., Qin, R., Chen, X.: Unmanned aerial vehicle for remote sensing applications—a review. Remote Sens. 11, 1443 (2019)

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