Smart farming based on Internet of Things (IoT) technologies enables crop farmers to collect real-time data related to irrigation and plant protection processes, aiming to increase production volume, improve product quality, and predict diseases, while optimizing resources and farming processes. IoT devices can collect vast amounts of environmental, soil, and crop performance data, thus building time series data that can be analyzed to forecast and compute recommendations and deliver critical information to farmers in real time. In this sense, the added-value from the farmers’ perspective is that such smart farming techniques have the potential to deliver a more sustainable agricultural production, based on a more precise and resource-efficient approach in the complex and versatile agricultural environment. The aim of this study is to investigate possible advantages of applying the Smart Farming as a Service (SFaaS) paradigm, aiming to support small-scale farmers, by taking over the technological investment burden and offering next generation farming advice through the combined utilization of heterogeneous information sources. The overall results of the pilot application demonstrate a potential reduction of up to 22% on total irrigation needs and important optimization opportunities on pesticides use efficiency. The current work offers opportunities for innovation targeting and climate change adaptation options (new agricultural technologies), and could help farmers to reduce their ecological footprint.

This study presents the experimental testing of a gas-sensing array, for the detection of two commercially available pesticides (i.e., Chloract 48 EC and Nimrod), towards its eventual use along a commercial smart-farming system. The array is comprised of four distinctive sensing devices based on nanoparticles, each functionalized with a different gas-absorbing polymeric layer. As discussed herein, the sensing array is able to identify as well as quantify three gas-analytes, two pesticide solutions, and relative humidity, which acts as a reference analyte. All of the evaluation experiments were conducted in close to real-life conditions; specifically, the sensors response towards the three analytes was tested in three relative humidity backgrounds while the effect of temperature was also considered. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analyzed using the common statistical analysis tool Principal Component Analysis (PCA). The sensing array, being compact, low-cost, and highly sensitive, can be easily integrated with pre-existing crop-monitoring solutions. Given that there are limited reports for effective pesticide gas-sensing solutions, the proposed gas-sensing technology would significantly upgrade the added-value of the integrated system, providing it with unique advantages.

There is a growing need for remote identification of crop types, which is a serious issue for policymakers and statistical accountants (i.e., agricultural inspectors and government agencies), for verifying the degree of validity of the information concerning the area and the type of each crop being cultivated. In this work, remote Sentinel-2 imaging data was utilized for calculating average NDVI values, twice a month, for the period 2017–2020, for cotton, rice and olive trees. In addition, a machine learning algorithm was developed and the corresponding model was trained using the average NDVI values. Python programming and KNN machine learning on the PyCharm environment were used.

This work presents the development of an automated and portable monitoring system for the point-of-need detection of tebuconazole and lambda-cyhalothrin. The system features nanoparticle/aptamer-modified electrochemical sensors that are integrated into a microfluidic chip based on polydimethylsiloxane (PDMS). More specifically, rapid and selective detection of both pesticides is achieved using target-specific aptamers immobilized on two-dimensional platinum nanoparticle films that serve as expanded nano-gapped electrodes to enhance sensor sensitivity. The effect of the device substrate (i.e., silicon versus flexible substrates) and measurement setup on biosensing performance has also been investigated. The final monitoring system is characterized by high sensitivity and selectivity in the cases of both target analytes and substrates. Τhe system features a limit of detection of 9.85 pM for tebuconazole, which is one of the lowest reported values in the literature; for lambda-cyhalothrin, it is worth noting that the results reported herein represent one of the few studies on an electrochemical aptamer-based sensor for this analyte, featuring a limit of detection of 48.5 pM. The system is also capable of selectively detecting both targets for complex cross-reactive sample matrices consisting of commercially available pesticides. Moreover, its use could be expanded to detect additional pollutants by functionalizing the biosensor surface with appropriate aptamers.

Nowadays, peach farms, and agriculture in general, face intensified challenges linked to pest control and irrigation needs, due to the effects of climate change. A contemporary and effective approach to these challenges is presented herein, which is based on the utilization of a smart farming system specialized in peach cultivation, in the framework of the SmartPeach project. A significant feature of the proposed smart farming system is its service-based approach, as its application does require technological investments from the farmers. The current research indicates a potential reduction of up to 25% of the total production cost due to optimization of irrigation and pest control applications, as well as an increase in farmers’ income up to 10% linked to improved product quality and yield.