Abstract
Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier.

Abstract
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

Abstract
The increasing world population, growing need for agricultural products, and labour shortages have driven the growth of robotics in agriculture. Tasks such as fruit harvesting require extensive hours of work during harvest periods and can be physically exhausting. Autonomous robots bring more efficiency to agricultural tasks with the possibility of working continuously. This paper proposes a stackable 3 DoF SCARA manipulator for tomato harvesting. The manipulator uses a custom electronic circuit to control DC motors with an endless gear at each joint and uses a camera and a Tensor Processing Unit (TPU) for fruit detection. Cascaded PID controllers are used to control the joints with magnetic encoders for rotational feedback, and a time-of-flight sensor for prismatic movement feedback. Tomatoes are detected using an algorithm that finds regions of interest with the red colour present and sends these regions of interest to an image classifier that evaluates whether or not a tomato is present. With this, the system calculates the position of the tomato using stereo vision obtained from a monocular camera combined with the prismatic movement of the manipulator. As a result, the manipulator was able to position itself very close to the target in less than 3 seconds, where an end-effector could adjust its position for the picking.

Abstract
Very few developments have been done to provide electric power supply of instrumented hip prosthesis. Actually, vibration-powered generators are the most appropriate mechanisms for this kind of application's environment. This paper describes the first attempt to develop the concept of energy harvesting from multiple energy sources applied in the same hip implant. Exploiting the potential of the three angular movements over the femoral component, namely in the abduction-adduction, flexion-extension and inward-outward rotation axes, three in board vibration-based mechanisms were developed in order to ensure electric power supply from multiple energy sources. A total of 53.7 mu J/s was harvested by a translation movement-based electromagnetic energy generator when a sinusoidal function with an amplitude of 40 mm and a frequency of 4 Hz was applied. A rotation movement-based electromagnetic energy generator has harvested 0.77 mu J/s when a sinusoidal function with an amplitude of 60 degrees and a frequency of 2.5 Hz was used. The piezoelectric energy harvester has achieved 0.6 mu J/s with the application of a sinusoidal function with an amplitude of 200 N and a frequency of 4 Hz. Besides, its ability of being fully autonomous, operating without expiry and maintenance, while offering safety during its entire lifetime are relevant features. This paper should provide the basis for the development of smart hip prosthesis with the ability to fix the aseptic implant loosening problem.

Abstract
Based on cyclic physiologic animal bioimpedance and body temperature a new method is being evaluated to predict estrus in dairy cattle with the aim of improving artificial insemination efficiency. Preceding in-vivo tests are being performed with a dedicated system based on the impedance converter AD5933, and a two-electrode configuration bioimplantable capsule. Acquired results will allow the optimal design of an implantable and autonomous low-power system. The implantable microsystem is being designed and simulated employing low-power techniques in a 0.35 mu m CMOS standard process.

Abstract
Silvopastoralism is an activity with multiple benefits from both the ecological and economical points of view. Besides the potential cash flow to landowners, it has multiple environmental advantages and it can even help to reduce fire hazard in woodland. This hazard reduction is due to cattle grazing, and for it to become effective, it is needed to move herds to the correct place at the correct time. Only knowing when and where shrubs and sward are ready for grazing it is possible to make an effective management of herd placement. In loco inspection of the terrain can be time and resource consuming, as large areas must be verified. An automated system to detect the presence of shrubs and sward can help to improve the management of herds. Since Wireless Sensor Networks (WSN) have spread in the last years and, are becoming very popular in agricultural applications, the objective of this work is to analyse the effect of vegetation in radio-frequency (RF) signals propagation, and use it to detect plants growth. Experiments showed that by measuring and analysing the attenuation in wireless links it is possible to detect plants growth. Besides providing the infrastructure to transmit data from field wireless sensors, the network itself can be used as the sensor.