Application of Machine Learning and Data Analysis in Agriculture
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 28562
Special Issue Editors
Interests: soil fertility; plant nutrition; crop growth modelling; machine learning; deep learning; plant and soil analysis
Interests: remote sensing and GIS; precision agriculture; erosion modelling; land use mapping; environmental impact assessment; fractal analysis
Special Issues, Collections and Topics in MDPI journals
Interests: spatial analysis; machine learning; geostatistics; soil data; environmental studies
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advancements in machine learning (ML) enable the identification of patterns within data, facilitating automated predictions of future events and aiding decision-making. ML algorithms excel at detecting nonlinear relationships and complex data structures, frequently encountered in environmental and agricultural data. Concurrently, the integration of ML with data from modern agricultural technologies, such as yield mappers, unmanned aerial vehicles, and satellites, provides valuable information, difficult to model with traditional statistical techniques.
This Special Issue focuses on reporting advances in machine learning applications for agriculture. It encompasses the application of ML and deep learning algorithms for predictive modeling and pattern detection in agricultural data. The importance of enhancing data analysis for real-world ML applications in agriculture has grown significantly due to the increasing global demand for high-quality and safe food.
Research topics may include, but are not limited to:
- Nitrogen, phosphorus, and potassium fertilization in field and greenhouse crops.
- Prediction of food quality and factors influencing it using agricultural data.
- Crop disease detection and prediction of disease risks.
- Monitoring crop water conditions.
- Predicting the impact of weather conditions on crop health and yield.
- Precision agriculture applications based on data collected with agricultural equipment.
- Monitoring and forecasting the effects of climate change on agriculture.
- Weed detection using computer vision techniques.
Dr. Miltiadis Iatrou
Dr. Christos Karydas
Dr. Panagiotis Tziachris
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- crop growth
- yield prediction
- food quality
- precision agriculture
- remote sensing
- crop health
- crop fertilization
- deep learning
- time series analysis
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