Urban agriculture can be shortly defined as the growing of plants and/or the livestock husbandry in and around cities. Although it has been a common occupation for the urban population all along, recently there is a growing interest in it both from public bodies and researchers, as well as from ordinary citizens who want to engage in self-cultivation. The modern citizen, though, will hardly find the free time to grow his own vegetables as it is a process that requires, in addition to knowledge and disposition, consistency. Given the above considerations, the purpose of this work was to develop an economic robotic system for the automatic monitoring and management of an urban garden. The robotic system was designed and built entirely from scratch. It had to have suitable dimensions so that it could be placed in a balcony or a terrace, and be able to scout vegetables from planting to harvest and primarily conduct precision irrigation based on the growth stage of each plant. Fertigation and weed control will also follow. For its development, a number of technologies were combined, such as Cartesian robots’ motion, machine vision, deep learning for the identification and detection of plants, irrigation dosage and scheduling based on plants’ growth stage, and cloud storage. The complete process of software and hardware development to a robust robotic platform is described in detail in the respective sections. The experimental procedure was performed for lettuce plants, with the robotic system providing precise movement of its actuator and applying precision irrigation based on the specific needs of the plants.

Improving environmental sustainability while maintaining economic viability is a major challenge for Mediterranean cereal production, where conventional systems are associated with high input use, elevated greenhouse gas emissions, and strong cost pressures. Although Conservation Agriculture (CA) and Precision Agriculture (PA) are widely promoted as promising solutions, evidence on their combined environmental and economic performance under real farming conditions remains limited. This study evaluated CA, PA, and their combined application (CPA) in winter cereal systems in Greece, using three years of farmer-managed field data from four representative sites. Agronomic and environmental performance and economic outcomes were assessed under actual farm sizes and a scaled 300 ha scenario. Across sites and years, no systematic yield differences were observed among CA, PA, and CPA, indicating that alternative systems can maintain yield stability under real farmer-managed conditions. Environmental performance was driven primarily by tillage intensity: CA reduced CO₂eq emissions by 212–238 kg ha⁻¹ relative to conventional tillage, while CPA achieved the largest reductions (262–332 kg ha⁻¹), accompanied by surface-layer SOM increases of 0.30–0.56% over three years. PA applied within conventional tillage resulted in only modest emission reductions (41–82 kg ha⁻¹), but consistently improved NUE, with variable-rate fertilization increasing NUE by approximately 5–7% relative to uniform application. Despite these environmental benefits, economic performance remained constrained due to high fixed machinery costs, high input prices, and low grain values resulting in negative net profits across all systems. CA reduced total costs by 3.8–11.8%, PA delivered only marginal improvements, while CPA achieved the largest cost reductions (5.0–12.6%) delivering also the most stable net profit mitigation. Carbon credit revenues increased profitability by only 2–3%. Scaling to 300 ha improved competitiveness through fixed-cost dilution, but profitability remained unattainable. Overall, integrated CA–PA systems offer substantial environmental benefits but require targeted policy support, cooperative machinery use, and service-based solutions to enable economically viable adoption in Mediterranean cereal systems.