Applied Agri-Technologies for Agriculture 4.0--Part I  
[PERSON]\({}^{\ast}\)\({}^{\copyright}\) and [PERSON]\({}^{\ast}\)\({}^{\copyright}\)  
Institute for Bio-Economy and Agri-Technology (iBO), Centre of Research and Technology-Hellas (CERTH),  
6 th km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS] (D.K. ); [EMAIL_ADDRESS] (D.B.) Agriculture plays a vital role in the global economy, with much of the rural population in developing countries depending on it. The depletion of natural resources makes the improvement of agricultural production more important but also more difficult than ever [1]. In this context, effective interventions in agriculture are essential for the fulfillment of its vital role, which is to maintain rural social and economic stability within sustainable development. The extensive use of applied agri-technologies results in productivity improvement and better resource use and reduces the time needed for farm management, marketing, logistics, and quality assurance. This Special Issue aimed to collect and present breakthrough research on various recent advances on processes in the field of applied agri-technologies. A total of 20 articles (including 14 original research papers and 6 review papers) are presented in this Special Issue, covering various fields in agriculture engineering discipline, including application in the agricultural production of:  
- machine learning;  
- automation and control;  
- information technology;  
- traceability, robotics;  
- human-machine interaction;  
- operations management;  
- technology adoption. Day-to-day farming operations include numerous tasks of detection, diagnosis, and prediction, providing a domain for a wide implementation of artificial intelligence and machine learning applications [2, 3]. [PERSON] et al. [4] presents an extensive review of state-of-the-art methods for plant-water-stress estimations that implement the deep learning (DL) sensor fusion approach, along with future prospects and challenges in the application field. It is evident that the DL's high learning capability is useful for the fast processing of field data originated from multi-source sensors. Furthermore, combining DL with an advanced data type such as 3D image can be considered for increasing this accuracy. However, sufficient datasets and variations of samples are required for creating robust methods. [PERSON] et al. [5] developed a robust convolutional neural network (CNN) model that is able to classify images of leaves, depending on whether or not these are infected by anthracose. The best performing algorithm was compared with a series of state-of-the-art CNNs commonly used for image classification problems. The results saw that the developed algorithm for the problem performed equally, and in some cases even better, compared to these algorithms. [PERSON] et al. [6] presents the applications of artificial neural networks (ANNs) in greenhouse technology, and how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT). Different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in line with the Agriculture 4.0 implementation. In the area of information technology, [PERSON] et al. [7] focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple Precision Farming environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. As proof of concept, the functionality of the proposed system was studied in real conditions where some evaluation results regarding its performance were quoted. The next group of articles regards applications of automation and control in modern agriculture.