Author:
Pinto Antonio Alves,Zerbato Cristiano,de Souza Rolim Glauco
Publisher
Springer Science and Business Media LLC
Reference33 articles.
1. Abbas F, Afzaal H, Farooque AA, Tang S (2020) Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy 10:1046. https://doi.org/10.3390/agronomy10071046
2. Arnold CY (1995) The determination and significance of the base temperature in a linear heat unit system. Proceedings of the American Society for Horticultural Science 74:430–450
3. Barzin R, Pathak R, Lotfi H, Varco J, Bora GC (2020) Use of UAS multispectral imagery at different physiological stages for yield prediction and input resource optimization in corn. Remote Sens 12:2392. https://doi.org/10.3390/rs12152392
4. Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: a comprehensive updated review. Sensors 21:3758. https://doi.org/10.3390/s21113758
5. Canata TF, Wei MCF, Maldaner LF, Molin JP (2021) Sugarcane yield mapping using high-resolution imagery data and machine learning technique. Remote Sens 13:232. https://doi.org/10.3390/rs13020232