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: 15 May 2024 | Viewed by 12914
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Analyzing the Impact of Storm 'Daniel' and Subsequent Flooding on Thessaly's Soil Chemistry Through Causal Inference
Authors: Miltiadis Iatrou; Miltiadis Tziouvalekas; Alexandros Tsitouras; Elefterios Evangelou; Christos Noulas; Dimitrios Vlachostergios; Vassilis Aschonitis; George Arampatzis; Irene Metaxa; Christos Karydas; Panagiotis Tziachris
Affiliation: Soil and Water Resources Institute – Hellenic Agricultural Organization "DIMITRA", 57001 Thessaloniki, Greece
Abstract: The storm 'Daniel' has caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. Causal relationships between soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. Results of causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and available phosphorus (P-Olsen), as well as between sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25 and 30 cm). Then linear regression equations were fitted across the different levels of deposit depth to determine the effect of deposit depth on CaCO3, P, and Mn. The results show that there is a linear slope of 0.12, -0.16, and 0.13, for CaCO3, P, and Mn with deposit depth, respectively. Statistical analysis indicates that corn growing in soils with sediment over 10 cm requires a 31.8% increase in P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.