Application of Digital Agriculture and Machine Learning Technologies in Crop Production

Submission Deadline: 15 December 2025 (closed) View: 1010 Submit to Special Issue

Guest Editors

Dr. Magdalena Piekutowska

Email: magdalena.piekutowska@upsl.edu.pl

Affiliation: Department of Botany and Nature Protection, Institute of Biology, Pomeranian University in Słupsk, 22b Arciszewskiego St., 76-200 Słupsk, Poland

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Research Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; potato production; plant breeding; soil science; plant growth analysis

Dr. Patryk Hara

Email: phara@agrotechnology.pl

Affiliation: Agrotechnology, 4 Jagiellonów St., 73-150 Łobez, Poland

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Research Interests: artificial neural networks, yield prediction, machine learning, neural modeling, precision agriculture, soil management, plant extracts, seed treatment, multiple linear regression modeling

Prof. Dr. Gniewko Niedbała

Email: gniewko.niedbala@up.poznan.pl

Affiliation: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland

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Research Interests: artificial neural networks, artificial intelligence, machine learning, yield modeling, forecasting, precision agriculture, environmental sustainability, agriculture soil fertility, crop production, plant nutrition, decision support systems

Summary

This special issue aims to explore the application of digital agriculture and machine learning technologies in crop production, focusing on how emerging technologies can enhance agricultural efficiency and sustainability. We invite high-quality original research papers, reviews, and case studies from scholars in related fields, covering but not limited to the following topics:

1. Applications of digital agriculture technologies in monitoring and managing crop growth.

2. The role of machine learning algorithms in crop yield prediction and pest/disease detection.

3. Implementation and effectiveness of data-driven decision support systems in farming.

4. Integration of sustainable agricultural practices with digital solutions through case studies.

In recent years, the agricultural sector has faced unprecedented challenges, including climate change, rising global population, and the pressing need for sustainable food production methods. Traditional agricultural practices are often insufficient to meet the increasing demands for food while simultaneously preserving the environment. As a result, the integration of digital technologies and machine learning into agriculture has emerged as a critical area of research.

Digital agriculture encompasses a wide range of technologies that enable farmers to gather, analyze, and act on data related to crop growth, soil health, and weather patterns. These technologies facilitate precision farming, allowing for resource optimization, reduced waste, and enhanced productivity. Machine learning, in particular, plays a pivotal role in transforming vast amounts of agricultural data into actionable insights, helping farmers make informed decisions to improve yield while minimizing the use of chemical inputs.

The importance of this research area lies not only in its potential to enhance agricultural efficiency but also in its ability to contribute to sustainable development goals. By adopting data-driven approaches, farmers can implement practices that promote environmental stewardship and social equity, ensuring food security for future generations. This special issue aims to highlight the innovative methodologies and applications in digital agriculture and machine learning, fostering collaboration and knowledge sharing among researchers and practitioners.

The aim of this special issue is to explore the applications of digital agriculture and machine learning technologies in crop production, focusing on how emerging technologies can enhance agricultural efficiency and sustainability. We invite researchers to submit high-quality original research papers, reviews, and case studies that will contribute to the advancement of knowledge in this field. The scope includes both innovative applications of technologies and theoretical approaches to issues related to agriculture.

· Applications of digital agriculture technologies in monitoring and managing crop growth.

· The role of machine learning algorithms in crop yield prediction and pest/disease detection.

· Implementation and effectiveness of data-driven decision support systems in farming.

· Integration of sustainable agricultural practices with digital solutions through case studies.

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Published Papers