Application of Artificial Intelligence and Machine Learning in Agriculture

Author:

Marwaha Sudeep,Deb Chandan Kumar,Haque Md. Ashraful,Naha Sanchita,Maji Arpan Kumar

Publisher

Springer Nature Singapore

Reference70 articles.

1. Agastya C, Ghebremusse S, Anderson I, Vahabi H, Todeschini A (2021) Self-supervised contrastive learning for irrigation detection in satellite imagery. arXiv preprint arXiv:2108.05484

2. Ahila Priyadharshini R, Arivazhagan S, Arun M, Mirnalini A (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput & Applic 31(12):8887–8895

3. Aitkenhead MJ, Coull MC, Towers W, Hudson G, Black HIJ (2012) Predicting soil chemical composition and other soil parameters from field observations using a neural network. Comput Electron Agric 82:108–116

4. Alkhudaydi T, Zhou J (2019) Spikeletfcn: counting spikelets from infield wheat crop images using fully convolutional networks. In: International Conference on Artificial Intelligence and Soft Computing, pp 3–13

5. Archana K, Saranya KG (2020) Crop yield prediction, forecasting and fertilizer recommendation using voting based ensemble classifier. Int J Comput Sci Eng 7(5):1–4

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