Climate change has reduced the availability of good quality water for agriculture, while favoring the proliferation of harmful insects, especially in Mediterranean areas. Deploying IoT-based systems can help optimize water-use efficiency in agriculture and address problems caused by extreme weather events. This work presents an IoT-based monitoring system for obtaining soil moisture, soil electrical conductivity, soil temperature and meteorological data useful in irrigation management and pest control. The proposed system was implemented and evaluated for olive parcels located both at coastal and inland areas of the eastern part of Crete; these areas face severe issues with water availability and saltwater intrusion (coastal region). The system includes the monitoring of soil moisture and atmospheric sensors, with the aim of providing information to farmers for decision-making and at the future implementation of an automated irrigation system, optimizing the use of water resources. Data acquisition was performed through smart sensors connected to a microcontroller. Data were received at a portal and made available on the cloud, being monitored in real-time through an open-source IoT platform. An e-mail alert was sent to the farmers when soil moisture was lower than a threshold value specific to the soil type or when climatic conditions favored the development of the olive fruit fly. One of the main advantages of the proposed decision-making system is a low-cost IoT solution, as it is based on open-source software and the hardware on edge devices consists of widespread economic modules. The reliability of the IoT-based monitoring system has been tested and could be used as a support service tool offering an efficient irrigation and pest control service.

Leaf analysis is essential for diagnosing nutritional status and guiding fertilizer application. The present study aimed to investigate the appropriate time for leaf sampling and the effect of genotype on olive nutrition. We determined leaf nutrient concentrations of calcium (Ca), iron (Fe), and manganese (Mn) in five Greek (‘Amfissis’, ‘Chondrolia Chalkidikis’, ‘Koroneiki’, ‘Mastoidis’, and ‘Kalamon’) and one Spanish (‘Picual’) variety from May 2019 to April 2020. The concentrations of Ca, Fe, and Mn were significantly affected by genotype and season. The highest concentrations for all nutrients were determined in April, while the lowest were in May, June, and October. Leaf Ca concentration significantly increased progressively from May to September for all the varieties. Leaves of ‘Koroneiki’ had the highest Ca concentration. Iron concentrations were within the sufficiency thresholds for all the varieties during the whole experimental period, and ‘Mastoidis’ showed the highest concentration. Leaf Mn concentration for all the varieties increased from May to September with an instant decrease in June, apart from ‘Amfissis’. The varieties ‘Kalamon’ and ‘Chondrolia Chalkidikis’ were found to be above the Mn sufficiency threshold throughout the year. Variations among season and genotype depict the complexity of nutrient transportation in leaf tissues.

Due to the environmental impact and increasing cost of inorganic fertilizers, farmers are exploring alternative fertilization strategies. Tenebrio molitor, otherwise known as the mealworm, is one of the most widely reared insect species for the production of high-quality protein for animals and humans. Mealworm frass (MF), a nutrient-rich byproduct of Tenebrio molitor cultivation, presents a viable option for organic fertilization. To investigate the fertilizer potential of frass, a greenhouse pot experiment was conducted, comparing three levels of MF (MF1, MF2, and MF3 at 20, 40, and 80 g/L soil, respectively), organic compost (ORG), and inorganic fertilizer (FERT). MF gave comparable results to FERT in terms of the measured parameters of vegetation, flowering, and production. ORG also gave comparable results to FERT as far as flowering and production but had significantly lower height compared to it. The MF3 treatment significantly improved the average fruit weight and total yield by 19.56% and 30.81%, respectively, compared to the ORG treatment. The two highest doses of MF outperformed FERT in terms of leaf and soil nutrient status, while MF1 and ORG did not differ from it. Furthermore, MF3 yielded 20% greater fruit weight than MF1. However, MF1 was comparable to FERT in fruit weight, resulting in superior fruit color. These results support reduced-input agriculture by providing data for optimizing soil fertility and nutrient management in crops. The findings of this experiment suggest that MF is a viable alternative to inorganic fertilizers and organic compost for greenhouse cultivation of tomatoes. These results highlight the potential of MF as a circular, bio-based fertilizer capable of maintaining tomato productivity while improving soil fertility under protected cultivation systems.

In the Mediterranean area, Greece is considered as one of the most important olive producing countries. Specifically, olive (Olea europaea) is the main crop covering more than 75% of the total tree cultivation area, while about 45% of the total cultivated area is being irrigated. Groundwater is the main source of irrigated water in Greece, but the growing demand and the climate change are putting this resource in risk.  The design of an optimal irrigation plan, based on detailed measurements and modeling tools, can effectively contribute towards water saving with no loss on crop yield, in the area. In this study, an IoT-based decision-making system for the management of irrigation water resources of olive orchards in a small sub-basin of Lasithi, Crete, Greece is presented.  The system integrates monitoring of soil moisture and atmospheric parameters in four fields within the study area and modeling approaches, using the modules of MIKE-SHE model, to simulate water flow in the unsaturated zone at the sub-basin level.  Additionally, 45 soil samples (from 3 different soil depths) have been collected and analyzed from the study area for soil texture, bulk density, rock percentage, pH, organic matter, and mineral nutrients. After the successful calibration of the model (comparison of simulated against observed soil moisture values) the spatio-temporal representation of soil moisture is used as guidance for developing optimal irrigation scheduling, considering olive tree water requirements, for the entire study area (sub-basin), even for olive orchards with lack of monitoring equipment installation.ACKNOWLEDGEMENTSThe authors acknowledge contribution of the AgriCapture CO2 - Horizon 2020 project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004282

Irrigated agriculture is the world’s largest water consumer, while at the same time water resources are under increasing pressure from rapidly growing demands and climate change. In Greece, about 45% of the total cultivated area is being irrigated and groundwater is the main source of irrigated water supply. Due to the scarcity of fresh water, the islands of Greece face serious problems by saltwater intrusion in coastal aquifers. In these areas, it is a common practice to utilize saline groundwater in irrigated olive orchards. Thus, estimation of all water fluxes temporally and spatially within and out of the crop root zone, and evaluation of issues like salinity are necessary to fully assess the efficiency of irrigation systems and methods. Simulation models can be used to investigate these issues over several seasons and scenarios. In this study, HYDRUS 2D/3D was used to evaluate data measured during one season (2022) in an olive (Olea europaea) orchard in Crete, Greece. The model efficiency was assessed by comparing model simulations against the observations of θ and EC obtained by an IoT-based monitoring system installed in the frame of HORIZON 2020-Agricapture project in irrigated fields of the Merabello area (Eastern Crete). The system includes the monitoring of soil moisture and atmospheric sensors, providing information on irrigation scheduling to farmers. Three IoT-devices were established in the study field, connected with an advanced soil moisture, temperature, and electrical conductivity sensor Teros12 (METER group, Inc. USA) installed at 0.3 m depth. Meteorological data collection was possible through a weather station (Davis Vantage Pro2™) installed in the area. Comparison of simulated against observed θ and EC showed a good precision of HYDRUS 2D/3D model for olive trees irrigation, with the Nash-Sutcliffe and the Root Mean Square Error (RMSE) being within the acceptable ranges. Model results can be used to improve the decision-making IoT system and advise farmers on various aspects of irrigation under saline environment, as, for example, in scheduling irrigation events for leaching salts, to avoid crop damage.The authors acknowledge contribution of the AgriCapture CO2 - Horizon 2020 project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004282.