A \"narrative\" review approach was adopted to first provide the basic terminology over the technical aspects of mobile robots used in autonomous operations, and then the fundamental planning aspects of mobile robots in agricultural environments were identified. This research aimed at supporting the planning of mobile robots in agriculture through identifying and classifying a set of technical terms and basic planning attributes. In field operations, \"field efficiency\" is a time-based measure for the productivity of an operational system directly connected with the economic performance of the operating system [17]. [PERSON] et al. [18] presented a new, more objective and measurable index for the field efficiency based on the travelled distance of an agricultural machinery during a field operation, the field traversing efficiency (FTE), as a function of well-quantified operational specifications. To show the degree of the dependence of the FTE index on the operational features, 864 scenarios derived from the consideration of 6 sample field shapes, 3 conventional fieldwork patterns, 4 driving directions, and 12 combinations of machine unit kinematics and implement width were evaluated by the developed tool. The test results showed that variation in FTE was up to 23% in the tested scenarios when using different operational setups. This approach on measuring field efficiency is very important for the evaluation of advanced operational approaches on field operations planning [19; 20]. As it has been considered by the agri-robotics research community, an intermediate step before the implementation of fully autonomous systems in the field is the cooperating and synergetic systems between human and robots [21; 22]. However, to understand and be prepared for such applications, existing knowledge on the connection between machine and human workers in agricultural operations must be reported and analyzed. In this Special Issue, two review papers are dedicated to ergonomic issues in field operations. [PERSON] et al. [23] presented a systematic review regarding the identification of the key risk factors associated with musculoskeletal disorders (MSDs) coming from manual agricultural operations. Additionally, the main root causes were determined along with the evaluation of the current ergonomic interventions. As a second part of this work, [PERSON] et al. [24] performed a review of the recent scholarly literature on ergonomics in agricultural mechanized operations. The results demonstrated that ergonomics in agriculture is an interdisciplinary topic where more collaborative efforts among physicians, ergonomists, engineers, and manufacturers are necessary for creating new ergonomic technologies and increasing the awareness of workers for the involved risk factors. Regarding targeted applications of human-machine cooperation in agriculture, [PERSON] et al. [25] presented a methodology to properly identify human activities related to the task of lifting a crate and placing it onto a robot suitable for agricultural operations with the use of machine learning algorithms for sequential data classification. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. These results confirmed the applicability of the proposed methodology for human awareness purposes in agricultural environments. In the area of agri-food chains and related traceability issues, [PERSON] et al. [26] presented an extensive literature review on the integration of blockchain into traceability systems. Initially, the review presented definitions, levels of adoption, tools, and advantages of traceability systems, accompanied by a brief overview of the functionality and advantages of blockchain technology. An extensive literature review on the integration of blockchain into traceability systems was conducted while existing commercial applications are presented, highlighting the relevant challenges and prospects of the application of blockchain technologies in the agri-food supply chain.