Applications of Machine Learning Technology in Agricultural Data Mining
Deadline for manuscript submissions: 30 June 2024 | Viewed by 331
Interests: machine learning; statistical learning; representation learning
Interests: food quality; bioactive compounds; fatty acids; antioxidants; functional foods; feed; food; animals; nutrition
Special Issues, Collections and Topics in MDPI journals
Currently, there is an imperative need for machine learning technology integration within the agricultural sector to optimize processes, productivity, and resource allocation, as well as to analyse, quantify, monitor, and enhance the overall sustainability of agricultural practices.
However, we can go further with the realization of machine learning's applications and data analysis capabilities, as it can be used to address modern agricultural challenges spanning cost forecasting, predictions pertaining to agricultural outputs, efficient livestock management, informed soil strategies, and precise production planning, in addition to extending to food product quality and insightful consumer analytics as well as labour reduction.
Amid global food security concerns and a heightened awareness of consumption patterns, this discourse places emphasis on employing machine learning techniques to enhance agricultural product quality for interpreting extensive datasets, revealing quantitative outcomes and intricate interrelationships among variables. Therefore, fostering the research and development of machine learning applications in agriculture becomes paramount, uniting researchers from diverse scientific disciplines to deliberate upon this shared interest.
This Special Issue focuses on the role of machine learning technologies in agricultural data mining, with the aim to share quality research concerning its applications in the diverse agriculture sector, including any type of crop production, livestock, storage, or food predictions.
Dr. Basarab Matei
Dr. Petru Alexandru Vlaicu
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.
- machine learning
- data mining
- crop management/prediction
- quality assessment
- storage conditions
- product quality
- food quality
- livestock management/production
- soil management
- production planning
- consumer analytics
- smart agricultural industry
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: Set-up of an autonomous platform for agricultural weed control
Authors: Michele Raffaelli
Affiliation: Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy
Title: Application of predictive analytics for waste reduction in the food sector
Authors: Pedro C. Santana-Mancilla1; Raymundo Buenrostro-Mariscal1; Irma L. Galván-Espinoza1; César J. Ramírez-Manzo1; Luis E. Anido-Rifón2, *
Affiliation: 1 School of Telematics, Universidad de Colima, Colima 28040, Mexico 2 atlanTTic Research Center, School of Telecommunications Engineering, University of Vigo, 36310 Vigo, Spain
Abstract: Food waste is a global issue with significant economic, environmental, and ethical implications. The integration of machine learning (ML) and predictive analytics presents an opportunity to tackle this challenge effectively. This article proposes to examine the use of ML algorithms in forecasting food demand and spoilage, thus enabling more efficient food consumption and distribution. By analyzing patterns in consumer behavior, supply chain bottlenecks, and perishability of products, ML can inform smarter decision-making that reduces food waste.