Machine Learning Applications in Digital Agriculture
The journal Agronomy (ISSN 2073-4395, IF 2.259) is currently running a Special Issue entitled "Machine Learning Applications in Digital Agriculture”. Prof. Dr. Thomas Scholten, Dr. Ruhollah Taghizadeh-Mehrjardi and Dr. Karsten Schmidt are serving as Guest Editors for this issue.
Machine learning—the scientific field that gives machines the ability to learn without being strictly programmed—can make agriculture more efficient and effective. An increasing amount of sophisticated data, from remote sensing and especially from proximal sensing, make it possible to bridge the gap between data and decisions within agricultural planning. On-demand representative sampling and modeling of useful soil information in an unprecedented resolution leads to an improvement in the decision-making processes of, for example, liming, irrigation, fertilization, higher productivity, reduced waste in food, and biofuel production. Additionally, sustainable land management practices are only as good as the data they are made of, and help to minimize negative consequences like soil erosion, soil compaction, and organic carbon and biodiversity loss. In the last few years, different machine learning techniques (e.g., artificial neural networks, decision tree, support vector machine, ensemble models, deep learning), different geophysical sensor platforms, as well as newly available satellite data have been tested and applied in precision agriculture. This Special Issue on Machine Learning Applications in Digital Agriculture provides international coverage of advances in the development and application of machine learning for solving problems in agriculture disciplines like soil and water management. Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers on topics pertaining to advances in the use of machine learning in agriculture are particularly welcomed. For further reading, please follow the link to the Special Issue Website at: