Politicians and the general public are concerned about climate change, water scarcity, and the constant reduction in agricultural land. Water reserves are scarce in many regions in the world, negatively affecting agricultural productivity, which makes it a necessity to introduce sustainable water resource management. Nowadays, there is a number of commercial IoT systems for irrigation scheduling, helping farmers to manage and save water. However, these systems focus on using the available fresh water sources, without being able to manage alternative water sources. In this study, an Arduino-based low-cost IoT system for automated irrigation scheduling is developed and implemented, which can provide measurements of water parameters with high precision using low-cost sensors. The system used weather station data combined with the FAO56 model for computing the water requirements for various crops, and it was capable of handling and monitoring different water streams by supervising their quality and quantity. The developed IoT system was tested in several field trials, to evaluate its capabilities and functionalities, including the sensors’ accuracy, its autonomous controlling and operation, and its power consumption. The results of this study show that the system worked efficiently on the management and monitoring of different types of water sources (rainwater, groundwater, seawater, and wastewater) and on automating the irrigation scheduling. In addition, it was proved that the system is can be used for long periods of time without any power source, making it ideal for using it on annual crops.

Wireless sensor networks (WSN) have found wide applications in many fields (such as agriculture) over last few years, and research interest is constantly increasing. However, power supply to the sensor nodes remains an issue to be resolved. Batteries are usually used to power the sensor nodes, but they have a limited lifetime, so solar energy harvesters are a good alternative solution. This study provides a comparative analysis between battery and solar energy harvesters for sensor nodes used for soil water monitoring. Experimental results show that small-sized solar panels with low-power energy harvester circuits and rechargeable batteries distinctly outperform secondary batteries in outdoor and continuous-operation applications. The power level of the energy storage device of sensor node 1, which was powered by a small PV panel, remained constantly close to 90% for all days. The power of the other three nodes, which were powered by a rechargeable battery, was initially at 100% of the charge and gradually started to reduce. Sensor node 1 performed a total of 1288 activations during the experimental period, while sensor nodes 2 and 4 behaved satisfactorily and performed a total of 781 and 803 activations, respectively. In contrast, sensor node 3 did not exhibit the same behavior throughout the experiments.

Various diseases and pests cause serious damage to vegetable crops during the growing season and after harvesting. Growers attempt to minimize losses by protecting their crops, starting with seed and seedling treatments and followed by monitoring their stands. In many cases, synthetic pesticide treatments are applied. Integrated pest management is currently being employed to minimize the impact of pesticides upon human health and the environment. Over the last few years, “smart” approaches have been developed and adopted in practice to predict, detect, and quantify phytopathogen occurrence and contamination. Our review assesses the currently available ready-to-use tools and methodologies that operate via visual estimation, the detection of proteins and DNA/RNA sequences, and the utilization of brand-new innovative approaches, highlighting the availability of solutions that can be used by growers during the process of diagnosing pathogens.

Heat stress (HS) suppresses voluntary feed intake in lactating sows, yet individual-level characterization of indoor and outdoor Temperature–Humidity Index (THI) linked to feed intake is rare in Mediterranean commercial settings. This prospective observational study enrolled 272 F1-crossbred sows (parities 1–7) farrowing from May–October 2025 at a commercial Greek farm. Outdoor THI was computed from 8760 hourly records and indoor THI from calibrated dataloggers; individual daily lactation feed intake (days 1–30) was recorded via RFID transponder-feeders. Building cooling (ΔTHI = 3.7–14.3) attenuated but did not eliminate mild-to-moderate indoor stress from June–September. Only parity and stillbirth count were Bonferroni-significant correlates of feed intake (parity: β = +0.142, p = 0.002). A sow-day Gradient Boosting model, evaluated by grouped cross-validation to prevent within-sow leakage, achieved CV-R² = 0.406 ± 0.045, providing a validated, herd-tested framework for parity-stratified feeding management, pending external validation across farms, genetics, and climates.

Livestock production occupies a paradoxical position with respect to climate change: farm animals are highly vulnerable to heat stress, feed and water scarcity, and climate-sensitive disease, while the sector contributes an estimated 14.5% of anthropogenic greenhouse gas emissions, most of which is biogenic methane from enteric fermentation. This review evaluates the evidence for precision livestock farming (PLF)—continuous, automated, real-time monitoring of individual animals’ health, welfare, production and environmental impact—across dairy and beef cattle, small ruminants, pigs, and poultry. For mitigation, precision feeding and additive-dosing strategies have been associated with enteric methane reductions of approximately 10–25%; for adaptation, wearable and non-invasive sensors have been reported to detect heat-stress-related behavioural changes before productivity losses become apparent, and smart climate-control systems have been associated with housing energy-use reductions of roughly 5–10%. Much of this evidence derives from single-farm, small-sample or short-duration studies and should be read as indicative rather than generalisable. Adoption remains constrained by high investment costs, limited interoperability, insufficient technical support, and uneven applicability to extensive and smallholder systems. We conclude that PLF is a valuable enabling technology that, combined with genetic, nutritional, and management strategies, can strengthen the resilience and environmental sustainability of livestock systems under a changing climate.