In Vivo Phenotyping for the Early Detection of Drought Stress in Tomato

Drought stress imposes a major constraint over a crop yield and can be expected to grow in importance if the climate change predicted comes about. Improved methods are needed to facilitate crop management via the prompt detection of the onset of stress. Here, we report the use of an in vivo OECT (organic electrochemical transistor) sensor, termed as bioristor, in the context of the drought response of the tomato plant. The device was integrated within the plant's stem, thereby allowing for the continuous monitoring of the plant's physiological status throughout its life cycle. Bioristor was able to detect changes of ion concentration in the sap upon drought, in particular, those dissolved and transported through the transpiration stream, thus efficiently detecting the occurrence of drought stress immediately after the priming of the defence responses. The bioristor's acquired data were coupled with those obtained in a high-throughput phenotyping platform revealing the extreme complementarity of these methods to investigate the mechanisms triggered by the plant during the drought stress event

Recent advances in the development of high-resolution 3D cadmium zinc telluride drift strip detectors

In the last two decades, great efforts have been made in the development of 3D cadmium-zinc-Telluride (CZT) detectors operating at room temperature for gamma-ray spectroscopic imaging. This work presents the spectroscopic performance of new high-resolution CZT drift strip detectors, recently developed at IMEM-CNR of Parma (Italy) in collaboration with due2lab (Italy). The detectors (19.4 mm × 19.4 mm × 6 mm) are organized into collecting anode strips (pitch of 1.6 mm) and drift strips (pitch of 0.4 mm) which are negatively biased to optimize electron charge collection. The cathode is divided into strips orthogonal to the anode strips with a pitch of 2 mm. Dedicated pulse processing analysis was performed on a wide range of collected and induced charge pulse shapes using custom 32-channel digital readout electronics. Excellent room-Temperature energy resolution (1.3% FWHM at 662 keV) was achieved using the detectors without any spectral corrections. Further improvements (0.8% FWHM at 662 keV) were also obtained through a novel correction technique based on the analysis of collected-induced charge pulses from anode and drift strips. These activities are in the framework of two Italian research projects on the development of spectroscopic gamma-ray imagers (10-1000 keV) for astrophysical and medical applications

Potentialities of CdZnTe Quasi-Hemispherical Detectors for Hard X-ray Spectroscopy of Kaonic Atoms at the DAΦNE Collider

Kaonic atom X-ray spectroscopy is a consolidated technique for investigations on the physics of strong kaon-nucleus/nucleon interaction. Several experiments have been conducted regarding the measurement of soft X-ray emission (20 keV) from intermediate kaonic atoms (carbon, aluminum, and sulfur). In this context, we investigated cadmium-zinc-telluride (CdZnTe or CZT) detectors, which have recently demonstrated high-resolution capabilities for hard X-ray and gamma-ray detection. A demonstrator prototype based on a new cadmium-zinc-telluride quasi-hemispherical detector and custom digital pulse processing electronics was developed. The detector covered a detection area of 1 cm(2) with a single readout channel and interesting room-temperature performance with energy resolution of 4.4% (2.6 keV), 3% (3.7 keV), and 1.4% (9.3 keV) FWHM at 59.5, 122.1, and 662 keV, respectively. The results from X-ray measurements at the DAfNE collider at the INFN National Laboratories of Frascati (Italy) are also presented with particular attention to the effects and rejection of electromagnetic and hadronic background

Environmental Monitoring with Visuo-Haptic Augmented Reality UAV Teleoperation

We report the field evaluation of a haptic guidance system for Unmanned Aerial Vehicles (UAV) in the context of environmental monitoring tasks. In the proposed system, the operator receives a haptic feedback based on the real-time measured intensity of the substance of interest. The haptic system, supplemented with visual cues integrated in an Augmented Reality interface, has been tested in monitoring tasks in relevant and operational environments. It has shown its potential to reduce the exploration time of large areas, as well as to decrease the mental fatigue experienced by the operator teleoperating the UAV in critical tasks.https://youtu.be/Ljahp0upYYk https://youtu.be/UOJaX4fssR

Leveraging Incremental Decision Trees and In-Vivo Biosensors for an Explainable Plant Health Monitoring System

Among the factors concerning plant development and agricultural yield, water stress and drought emerge as pivotal factors. Indeed, the ability to know in advance imminent water stress in crops based on measurable biochemical metrics is priceless, as it offers the opportunity for rapid interventions aimed at restoring optimal growth conditions before the plants show clear visible stress symptoms.In this work, we present an explainable system for smart agriculture focused on the continuous monitoring of the water stress condition of tomato plants, achieved through a new in-vivo biosensor, named bioristor. The proposed system embeds an incremental and explainable by design classifier. Specifically, we experimented with the traditional Hoeffding decision tree and its fuzzy version. This system analyzes the data received from bioristors to assess the health status of a tomato plant and classifies it into four classes. The proposed system also leverages an incremental learning technique, which allows the classification model to be updated during the monitoring period, to maintain adequate classification performance. In this way, the conditions of the plants are monitored continuously with an effective model, allowing for timely countermeasures to be taken if a water stress situation is detected. We present preliminary results on a real dataset, using four features related to the ionic currents within the plant sap, measured through bioristors. We assessed the system performance both in terms of classification ability and model complexity, obtaining promising results and the generation of interesting rules that could allow the implementation of effective countermeasures to keep the plants healthy as long as possible

Evaluation of electric field profile and transport parameters in solid-state CZT detectors

Electric field profile and transport parameters from independent measurements on planar CdZnTe spectroscopic detectors were compared. The mobility and lifetime for electrons, together with the electric field profile, were deduced from current transient profiles induced by laser pulses at different applied voltages. The method is founded on a procedure of minimization built up from the Ramo-Shockley theorem and some physical constraints. The procedure was tested on a planar detector built with spectroscopic CdZnTe grown at IMEM-CNR in Parma, Italy. The mobility-lifetime product was also evaluated by fitting the charge collection efficiency curves under a suitable electric field profile model. Comparison between results from both the techniques are in good agreement and confirm the high spectroscopic features of the investigated material

Gamma-Ray Spectral Unfolding of CdZnTe-Based Detectors Using a Genetic Algorithm

The analysis of γ-ray spectra can be an arduous task, especially in the case of room temperature semiconductor detectors, where several distortions and instrumental artifacts conceal the true spectral shape. We developed a genetic algorithm to perform the unfolding of γ-spectra in order to restore the true energy distribution of the incoming radiation. We successfully validated our approach on experimental spectra of four radionuclides (241Am, 57Co, 137Cs and 133Ba) acquired with two CdZnTe-based detectors with different contact geometries (single pixel and drift strip). The unfolded spectra consist of δ-like peaks in correspondence with the radiation emissions of each radioisotope

Detection of nuclear sources by UAV teleoperation using a visuo-haptic augmented reality interface

A visuo-haptic augmented reality (VHAR) interface is presented enabling an operator to teleoperate an unmanned aerial vehicle (UAV) equipped with a custom CdZnTe-based spectroscopic gamma-ray detector in outdoor environments. The task is to localize nuclear radiation sources, whose location is unknown to the user, without the close exposure of the operator. The developed detector also enables identification of the localized nuclear sources. The aim of the VHAR interface is to increase the situation awareness of the operator. The user teleoperates the UAV using a 3DOF haptic device that provides an attractive force feedback around the location of the most intense detected radiation source. Moreover, a fixed camera on the ground observes the environment where the UAV is flying. A 3D augmented reality scene is displayed on a computer screen accessible to the operator. Multiple types of graphical overlays are shown, including sensor data acquired by the nuclear radiation detector, a virtual cursor that tracks the UAV and geographical information, such as buildings. Experiments performed in a real environment are reported using an intense nuclear source

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Water stress and in particular drought are some of the most significant factors affecting plant growth, food production, and thus food security. Furthermore, the possibility to predict and shape irrigation on real plant demands is priceless. The objective of this study is to characterize, classify, and forecast water stress in tomato plants by means of in vivo real time data obtained through a novel sensor, named bioristor, and of different artificial intelligence models. First of all, we have applied classification models, namely Decision Trees and Random Forest, to try to distinguish four different stress statuses of tomato plants. Then, we have predicted, through the help of recurrent neural networks, the future status of a plant when considering both a binary (water stressed and not water stressed) and a four-status scenario. The obtained results are very good in terms of accuracy, precision, recall, F-measure, and of the resulting confusion matrices, and they suggest that the considered novel data and features coming from the bioristor, together with the used machine and deep learning models, can be successfully applied to real-world on-the-field smart irrigation scenarios in the future

Development of an In Vivo Sensor to Monitor the Effects of Vapour Pressure Deficit (VPD) Changes to Improve Water Productivity in Agriculture

Environment, biodiversity and ecosystem services are essential to ensure food security and nutrition. Managing natural resources and mainstreaming biodiversity across agriculture sectors are keys towards a sustainable agriculture focused on resource efficiency. Vapour Pressure Deficit (VPD) is considered the main driving force of water movements in the plant vascular system, however the tools available to monitor this parameter are usually based on environmental monitoring. The driving motif of this paper is the development of an in-vivo sensor to monitor the effects of VPD changes in the plant. We have used an in vivo sensor, termed "bioristor", to continuously monitor the changes occurring in the sap ion's status when plants experience different VPD conditions and we observed a specific R (sensor response) trend in response to VPD. The possibility to directly monitor the physiological changes occurring in the plant in different VPD conditions, can be used to increase efficiency of the water management in controlled conditions thus achieving a more sustainable use of natural resources