Machine Learning Applications in Smart Agriculture

Dear Colleagues,

To deal with the rising challenges and barriers of the agricultural domain, the complex agricultural landscape needs to be better investigated and understood. In order for this to happen, every aspect of the agricultural ecosystem, including both pre-production (sowing, treatment, inputs) and post-production activities (harvesting, labeling, shipping) as well consumer behavior, produces some data that must be further analyzed.

Data gathering throughout the agricultural ecosystem has already been facilitated through the use of recent technological developments in Information and Communication Technology (ICT), such as the Internet of Things (IoT), radio-frequency identification (RFID) systems, wireless sensor networks (WSNs), and unmanned aerial vehicles (UAVs). However, the processing of all this information constitutes a major obstacle. The multi-collectiveness, extreme volume, and high velocity of this information, in combination with the complexity of the agricultural domain, forms a complicated system, which hinders modern agriculture reach its true potential.

In the coming years, big data are expected to pave the way for a productive and sustainable rural development as they offer unprecedented capabilities and can drive innovative concepts, such as smart farming, precision agriculture, and smart livestock. Toward this direction, machine learning technologies envision a wide range of innovative applications that offer considerable benefits to agronomists and farmers, such as enhanced production of high quality, efficient resource allocation, germinal disease recognition, climate change mitigation, income increase, cost and laborious task decrease, animal welfare, and more.

However, several of the aforementioned expectations have not been delivered yet, and additional research efforts are needed to utilize machine learning applications in the new era of smart farming. The purpose of this Special Issue is to publish high-quality research papers, as well as review articles in the emerging research field of machine learning applications in smart agriculture, which are addressing the following topics:

• Machine learning algorithms for predicting crop yield;
• Machine learning algorithms for predicting livestock production;
• Machine learning algorithms for demand and supply predictions in primary production;
• Machine learning and computer vision algorithms for early detection and diagnosis of a crop disease/anomaly;
• Machine learning algorithms for early detection and diagnosis of livestock diseases;
• Tools and approaches for crop health monitoring;
• Tools and approaches for livestock welfare monitoring;
• Tools and methods for providing advice and guidance to farmers based on their crops' responsiveness to input;
• Tools and approaches for field clustering based on crop conditions (production, damage, inputs);
• Machine learning and computer vision schemes for weed detection and fruit grading;
• Tools and approaches for combining multi-source structured and/or unstructured data;
• Machine learning and computer vision algorithms for species recognition;
• Concepts and approaches for data privacy and security in the agricultural domain;
• Tools and methodologies for conducting scientific models and simulations for environmental phenomena;
• Tools and methods for quantitative analysis of the interaction between crops and their environment.

Prof. Dr. Panagiotis Sarigiannidis
Dr. Thomas Lagkas
Dr. Alexandros-Apostolos A. Boulogeorgos
Guest Editors

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