Wheat yield prediction using machine learning and advanced sensing techniques

By X. Pantazi, Dimitrios Moshou, T. Alexandridis, Rebecca Louise Whetton, Abdul Mounem Mouazen. Computers and Electronics in Agriculture, 2016

516 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/d30b000d528af946bf93bd45c69324668fa6b17b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Qiangqiang Yuan
2. Dionysis D. Bochtis
3. Huanfeng Shen
4. Allard de Wit
5. Simon Pearson
6. Jun Xiong
7. M. Gumma
8. Prasad S. Thenkabail

In Environmental Engineering

1. Ioannis N. Athanasiadis
2. Christos Pylianidis
3. Sjoukje A. Osinga
4. Huanfeng Shen
5. Liangpei Zhang

In Plant Science

1. Dionysis D. Bochtis
2. Allard de Wit
3. Simon Pearson
4. Konstantinos G. Liakos
5. Dimitrios Moshou
6. Patrizia Busato
7. Athanasios Balafoutis

Citations in the corpus

• A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (2018, unknown): “…Zhou et al., 2016); (g) the Automated Cropland Classi fi cation Algorithms (Thenkabail and Wu, 2012; Teluguntla et al., 2017a; Waldner et al., 2015); and (h) Machine learning programming involving a combination of multiple methods (DeFries and Chan, 2000; Duro et al., 2012; Pantazi et al., 2016).”
• Automated cropland mapping of continental Africa using Google Earth Engine cloud computing (2017, methodology): “…et al., 2015), spectral matching techniques (Dheeravath et al., 2010), support vector machines (Mountrakis et al., 2011), random forest algorithm (Tatsumi et al., 2015) and a number of other machine learning algorithms (DeFries, 2000; Duro et al., 2012; Lary et al., 2016; Pantazi et al., 2016).”
• Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “Pantazi et al. (2016) predicted wheat yield variation within the fi eld based on multi-layer soil data and remote sensing crop growth features.”
• Machine Learning in Agriculture: A Review (2018, result): “Another study dedicated to yield prediction, and specifically to wheat yield prediction, was presented in another study [78].”
• Machine learning for large-scale crop yield forecasting (2020, methodology): “…deep neural networks to predict corn yield in the US; You et al. (2017) leveraged representation learning ideas to predict soybean yield in the US; and Pantazi et al. (2016) used self-organizing maps (Von der Malsburg 1973; Kohonen 2001) to predict within-field variation of wheat yield in the UK.”
• A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, background): “Crop yield prediction in agriculture is significant because it supports better crop management and plan marketing activities (Pantazi et al. 2016).”