Big Data Analytics in Agriculture
A special issue of AgriEngineering (ISSN 2624-7402).
Deadline for manuscript submissions: 31 August 2024 | Viewed by 5129
Special Issue Editors
Interests: statistical learning; big data analytics; intelligent systems in agriculture; smart farming
Interests: machine learning; data science; information representation
Special Issue Information
Dear Colleagues,
Modern agriculture is challenged to ensure food security by providing food, fiber and clean energy in a sustainable way. The growth of data generated by modern agriculture and the rapid adoption of different converging technologies – nanotechnology, biotechnology, information technology and cognitive science – to support the development of disruptive products and processes have become the main drivers of scientific discovery and innovation in digital agriculture.
As a result, agribusinesses are becoming larger and more diverse, which results in the growing volumes of complex data that has to be managed constantly. Such data include external data from social media, supplier network channels, and sensor/machine data from the field. This leads to the agricultural digital transformation, opening new opportunities. The technological revolution that is currently happening in the agricultural sector became possible due to, among other things, big data. Collecting and analyzing big data can not only improve the productivity of individual farms, but also help prevent a global food crisis.
The significance of the impact of big data in agriculture lies in the growing need to produce more food while using less land for it. To reach this goal, policymakers and industry leaders seek assistance from technological innovations, including big data, IoT, analytics, artificial intelligence, and cloud computing.
Another benefit from big data analytics practices is to help farmers and decision makers to save costs and open new business opportunities. Therefore, practical applications of big data analytics cover a broad spectrum of solutions in agriculture. Here are the main possibilities that come with big data use in agribusinesses: food safety, yield prediction, analysis of economically viable scenarios, pesticides use optimization, farm equipment management, supply chain problems management, risk analysis, and weight gain in animals, among others.
To effectively respond to these challenges, it is essential to participate in solid partnerships involving government, academia, and productive sector. These strengthened and expanded relationships will allow the insertion and expansion of digital technologies in agriculture, in a transversal way, as enablers of high-impact results, and will imprint the concept of digital agriculture to the most different areas in the agricultural sector.
This Special Issue is focused on showcasing original research on big data analytics in agriculture. We welcome submission of research and review articles as well as short communications. Contributions could include, but are not limited to:
- Strategic decision making;
- Classification and forecast of plant diseases;
- Identification and tracking of objects in agriculture;
- Energy–water–food nexus;
- Low carbon agriculture;
- Precision livestock farming;
- Connectivity and internet of things;
- Alert systems;
- Intelligent systems in agriculture;
- Agricultural and environmental modeling and monitoring;
- Robotics and autonomous systems;
- Climate risk;
- Agriculture and climate change;
- Land use and cover;
- Geospatial technologies and services;
- Drone technology for monitoring crops;
- Bioinformatics and biotechnology in agriculture;
- Agrochemicals use optimization;
- Farm equipment management;
- Supply chain problems management;
- Yield prediction;
- Food safety.
Dr. Stanley Robson De Medeiros Oliveira
Dr. Kleber Xavier Sampaio De Souza
Dr. Sônia Ternes
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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
- data analytics;
- agriculture 4.0;
- artificial intelligence;
- machine learning;
- computer vision;
- unmanned ground vehicles;
- precision agriculture;
- wireless sensor networks;
- agricultural machinery management;
- simulation and mathematical models;
- smart farming; decision support system;
- remote sensing;
- physical–cybernetic systems;
- landscape management.
