Computer-Aided Crop Yield Forecasting Techniques - Systematic Review Highlighting the Application of AI

Author:

Pushpalatha Raji,Roshni Thendiyath,Gangadharan Byju,Kutty Govindan

Publisher

Springer Science and Business Media LLC

Reference97 articles.

1. United Nations Department of Economic and Social Affairs, Population Division. (2022). World Population Prospects 2022: Summary of results. UN DESA/POP/2022/TR/NO. 3.

2. Arumugam, S., Ozkan, B., Aravind, J., & Mockaisamy, P. (2021). Impact of COVID-19 pandemic on global agriculture, livelihoods and food system. Journal of Agricultural Sciences (Tarim Bilimleri Tergisi), 27(3), 239–246.

3. Habanyati, E. J., Paramasivam, S., Seethapathy, P., Jayaraman, A., & Kedanhoth, R. (2022). Impact of COVID-19 on the agriculture sector: Survey analysis of farmer responses from Kerala and Tamil Nadu States in India. Agronomy, 12(2), 503.

4. Jain, R. C., & Agarwal, R. (1992). Probability model for crop yield forecasting. Biometrical Journal, 34(4), 501–511.

5. Jain, R. C., Agarwal, R., & Singh, K. N. (1992). A within year growth model for crop yield forecasting. Biometrical Journal, 34(7), 789–799.

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