D
Dimitrios Moshou
Researcher at Aristotle University of Thessaloniki
Publications - 128
Citations - 5633
Dimitrios Moshou is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Artificial neural network & Precision agriculture. The author has an hindex of 28, co-authored 121 publications receiving 3903 citations. Previous affiliations of Dimitrios Moshou include Catholic University of Leuven & United States Department of Agriculture.
Papers
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Machine Learning in Agriculture: A Review.
TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
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Review: Sensing technologies for precision specialty crop production
TL;DR: In this paper, the authors present a review of these sensing technologies and discuss how they are used for precision agriculture and crop management, especially for specialty crops, and some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed.
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Wheat yield prediction using machine learning and advanced sensing techniques
Xanthoula Eirini Pantazi,Dimitrios Moshou,Thomas Alexandridis,Rebecca L. Whetton,Abdul Mounem Mouazen +4 more
TL;DR: The aim of this paper is to predict within field variation in wheat yield, based on on-line multi-layer soil data, and satellite imagery crop growth characteristics, which shows that the Supervised Kohonen Networks model had the best overall performance.
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Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy
Antonios Morellos,Xanthoula Eirini Pantazi,Dimitrios Moshou,Thomas Alexandridis,Rebecca L. Whetton,Georgios Tziotzios,Jens Wiebensohn,Ralf Bill,Abdul Mounem Mouazen +8 more
TL;DR: In this article, the predictive performance of two linear multivariate and two machine learning methods for predicting soil total nitrogen (TN), organic carbon (OC), and moisture content (MC) was compared.
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The potential of optical canopy measurement for targeted control of field crop diseases
TL;DR: A review of recent developments in the use of optical methods for detecting foliar disease, evaluates the likely benefits of spatially selective disease control in field crops, and discusses practicalities and limitations of using optical disease detection systems for crop protection in precision pest management.