This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.

Due to the global water crisis there is a strong need for real-time water quality monitoring with high temporal and spatial resolution. This paper presents an economical multiparameter water quality monitoring system for continuous monitoring of fresh waters. It is based on a sensor node that integrates turbidity, temperature, and conductivity sensors, a miniature eighteen-channel spectrophotometer, and a sensor for the detection of thermotolerant coliforms, which is a major novelty of the system. Due to the influence of water impurities on the measurement of thermotolerant coliforms, a heuristic method has been developed to mitigate this effect. Moreover, the sensor is low power and with an integrated Long Range Wide Area Network module, it comprises a system that is wireless sensor network ready and can send data to a dedicated server. In addition, the system is submersible, capable of long-term field operation, and significantly cheaper in comparison to existing solutions. The purpose of the system is to give early warning of incidental pollution situations, thus enabling authorities to take action regarding further prevention of such occasions.

Robotics is making significant inroads into agriculture. Automated systems are now being used for tasks such as planting, weeding, and harvesting, increasing efficiency and reducing labor costs. A higher cost of robots can be justified for high-value crops. This paper presents a robotics system aimed at blueberry production and targeting precision low-volume weed spraying for the blueberries planted on ridges. Equipped with visually-based machine learning weed segmentation and clusterization algorithms, it represents a powerful tool for precise, targeted weed eradication with minimum impact on the environment. The simulation of the system operation is an indispensable step in developing complex systems, hence this paper presents simulation results.