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This manuscript reports the application of sensors for water use efficiency with a focus on the application of an in vivo OECT biosensor. In two distinct experimental trials, the in vivo sensor bioristor was applied in yellow kiwi plants to monitor, in real-time and continuously, the changes in the composition and concentration of the plant sap in an open field during plant growth and development. The bioristor response and physiological data, together with other fruit sensor monitoring data, were acquired and combined in both trials, giving a complete picture of the biosphere conditions. A high correlation was observed between the bioristor index (ΔIgs), the canopy cover expressed as the fraction of intercepted PAR (fi_PAR), and the soil water content (SWC). In addition, the bioristor was confirmed to be a good proxy for the occurrence of drought in kiwi plants; in fact, a period of drought stress was identified within the month of July. A novelty of the bioristor measurements was their ability to detect in advance the occurrence of defoliation, thereby reducing yield and quality losses. A plant-based irrigation protocol can be achieved and tailored based on real plant needs, increasing water use sustainability and preserving high-quality standards. [ABSTRACT FROM AUTHOR]

Ensuring quality control by identifying issues such as nutrient deficiencies and pest infestations helps in crop yield estimation for effective planning and resource management. However, the development of sensors for real-time continuous monitoring of fruit growth in an open environment has been a challenge. Herein, we propose a hydrophobic and stretchable piezoresistive sensor to monitor real-time fruit growth using graphene–silicone-based screen-printable paste (GSiCP) remotely. Graphene has been synthesized with a green approach utilizing an electrochemical exfoliation process, wherein no hazardous or strong acids were employed. The synthesized graphene has been characterized using X-ray (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), Raman spectroscopy, and dynamic light scattering. The developed (GSiCP) sensors have shown a high sensitivity, stretchability, and cyclic durability of 2050, 125%, and 5000 cycles, respectively. The sensors show an exceptional minimal detection limit of 0.04%, which shows the capability of monitoring the slowest fruit growth. The GSiCP sensors have been used to monitor on-field the continuous real-time growth of fruit (brinjal) for 15 days using the Internet of Things (IoT). Further, the impact of an excess amount of fertilizer (urea) on fruit growth and plants has been investigated. Owing to the sensor's merits, GSiCP sensors have shown applicability in monitoring physiological signals (radial artery pulse rate) and physical activities. [ABSTRACT FROM AUTHOR]

Significant crop losses are due to plant-parasitic nematodes. Nematicides are expensive and potentially toxic to men, the environment, and plants. This study evaluated the hidden phytotoxicity effects of nematicides in soybeans. Two soybean cultivars (8473RSF and M7198IPRO) were evaluated with five nematicide treatments (biological, cadusaphos, abamectin, fluensulfone, and an untreated control) for changes in chlorophylls, biometrics, and spectral (TGI visible spectral index captured with a smartphone camera) variables to determine and anticipate the identification of plant stresses. Evaluations occurred 33, 47, and 66 days after sowing (DAS). The a/b chlorophyll proportion was greatest for M7198IPRO and cadusaphos. The chlorophyll variables did not present significant interactions or differences at 47 DAS, indicating that possible nematicide effects were transient and should be evaluated earlier than 33 DAS. Leaf area, leaf mass, and shoot mass were smaller for 8473RSF and outstanding for abamectin and fluensulfone. The response of the spectral index did not present significant interaction among the factors; however, at 33 and 47 DAS, the index was low for 8473RSF and lowest for cadusaphos only at 33 DAS. The correlations between the spectral index and other variables were significant and moderate for soybean total leaf area. Although no apparent phytotoxicity symptoms caused by nematicides were observed, the visible vegetation index generated using a smartphone camera can still improve crop management solutions. [ABSTRACT FROM AUTHOR]

Early detection of the plant diseases is critical to avoid losses in the yield and quality of the agricultural product. The studies of the plant diseases have been widely researched to detect abnormality in plant growth using visually observable patterns on the plant. Plant monitoring and disease detection is needed to ensure sustainability in agriculture. However, it is usually very difficult to monitor the plant diseases manually as they require real-time and precision detection. Image processing is commonly used for the detection of plant diseases which involved image acquisition, pre-processing, segmentation, feature extraction and classification. In this paper, an Artificial Intelligence based image processing algorithm is proposed to detect diseases on a Chilli plant using its leaves images. The proposed system included ResNet CNN for feature extraction and classification. [ABSTRACT FROM AUTHOR]

This manuscript reports the application of sensors for water use efficiency with a focus on the application of an in vivo OECT biosensor. In two distinct experimental trials, the in vivo sensor bioristor was applied in yellow kiwi plants to monitor, in real-time and continuously, the changes in the composition and concentration of the plant sap in an open field during plant growth and development. The bioristor response and physiological data, together with other fruit sensor monitoring data, were acquired and combined in both trials, giving a complete picture of the biosphere conditions. A high correlation was observed between the bioristor index (ΔIgs), the canopy cover expressed as the fraction of intercepted PAR (fi_PAR), and the soil water content (SWC). In addition, the bioristor was confirmed to be a good proxy for the occurrence of drought in kiwi plants; in fact, a period of drought stress was identified within the month of July. A novelty of the bioristor measurements was their ability to detect in advance the occurrence of defoliation, thereby reducing yield and quality losses. A plant-based irrigation protocol can be achieved and tailored based on real plant needs, increasing water use sustainability and preserving high-quality standards.

Plant leaf temperature and its environmental parameters provide valuable information on plant growth. This paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition). The developed SCADA system monitors the leaf temperature and the air parameters of temperature and humidity, as well as the soil parameters of temperature, moisture, pH, electrical conductivity, nitrogen, phosphorous, and potassium. A novel method is proposed for measuring the leaf temperature using a low-cost 8 × 8 array thermal camera. The sensor systems in the field are developed to wirelessly communicate with the Hawell IoT Cloud HMI via a Modbus TCP protocol. To visualize the thermal image on the HMI dashboard, a novel approach is proposed wherein the data are transferred using the Modbus TCP protocol. The HMI is connected to a cloud server and can be accessed by the users using the web browser or mobile application on a smartphone. The experimental results show that the proposed hardware, software, and communication protocol are reliable for real-time and continuous plant monitoring. Further, the evaluation of sensor data shows that the data from the thermal camera and air parameters sensor can be independently interpreted. However, the data from the soil sensor should be interpreted in consideration of the other parameters. [ABSTRACT FROM AUTHOR]

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The availability of real‐world data in agricultural applications is of paramount importance to develop robust and effective robotic‐based solutions for farming operations. In this application context, however, very few data sets are available to the community and for some important crops, such as grapes and olives, they are almost absent. Therefore, the aim of this paper is to introduce and release ARD‐VO, a data set for agricultural robotics applications focused on vineyards and olive cultivations. Its main purpose is to provide the researchers with a real‐world extensive set of data to support the development of solutions and algorithms for precision farming technologies in the aforementioned crops. ARD‐VO has been collected with an unmanned ground vehicle (UGV) equipped with different heterogeneous sensors that capture information essential for robot localization and plant monitoring tasks. It is composed of sequences gathered in 11 experimental sessions between August and October 2021, navigating the UGV for several kilometers in four cultivation fields in Umbria, a central region of Italy. In addition, to highlight the utility of ARD‐VO, two application case studies are presented. In the first one, the data set is used to compare the performance of simultaneous localization and mapping and odometry estimation methods using vision systems, light detection and ranging, and inertial measurement unit sensors. The second one shows how the multispectral images included in ARD‐VO can be used to compute Normalized Difference Vegetation Index maps, which are crucial to monitor the crops and build prescription maps. [ABSTRACT FROM AUTHOR]

In the period 2017-2023, a survey for the assessment of the phytosanitary condition of sweet chestnut (Castanea sativa) was conducted in a permanent sample plot (PSP) Belasitsa, which is part of the European large-scale network for monitoring the health status of forest ecosystems under the International Co-operative Program 'Forests. The PSP is set in a natural chestnut stand in Belasitsa mountain at an altitude of 643 m. Data collected from the first years of the monitoring determined a slight deterioration in the health status of the chestnut trees caused by an infection with the fungal pathogen Cryphonectria parasitica. An improvement in trees' vitality and lack of active necrosis were observed in 2022-2023. On the wounds, initiation of callus along the wounded edges was reported. Currently, the intro duction of new and dangerous invasive insect pests Dryocosmus kuriphilus, Corythucha arcuata, etc., has not been detected. Attacks of both pests could further deteriorate the health status of the sweet chestnut in Belasitsa. [ABSTRACT FROM AUTHOR]

Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country's population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product. [ABSTRACT FROM AUTHOR]

Standard documents regarding inventories at enterprises releasing wastewater into a body of water or a central water treatment system are listed. Sources of water supply, means of water disposal, types of sewer, and methods of wastewater treatment are discussed. Recycling of conventionally treated water (meeting relevant standards) is required, either within the coke plant or outside of the enterprise, after treatment in accordance with environmental laws on the basis of the best available technology for enterprises of environmental impact category I. [ABSTRACT FROM AUTHOR]

Well-balanced nutrition is important for the successful cultivation of healthy plants. In this paper, we demonstrate a nondestructive optical method that can sense a deficiency of certain nutrients. The setup was tested on hydroponically grown Ocimum basilicum. The plants were subjected to nutrient deficiency by the exclusion of one of the essential elements (Fe, Mg, P, N) from the hydroponic solution. A control group of plants, fed by the balanced hydroponic solution, was also grown under the same conditions. The proposed method tracks and records the optical transmittance of the plants' leaves. All groups exhibit clearly defined day-night Circadian rhythms. When compared to the control group, the treated plants exhibited modified circadian rhythms of the optical transmission, suggesting an early indicator of the plants' stress. The condition of the plants under test was also assessed by the more common (destructive) methods such as: measurements of the determination of the photosynthetic pigment content, dray weight determination and the efficiency of PSII. Several biological parameters were observed, calculated and compared to the graphs of optical transmission dependence in real time. Presented results have demonstrated a significant potential of the proposed optical method for the early detection of plants' stress in hydroponic cultivation.

In this paper, we present GrowliFlower, a georeferenced, image‐based unmanned aerial vehicle time‐series dataset of two monitored cauliflower fields (0.39 and 0.60 ha) acquired in 2 years, 2020 and 2021. The proposed dataset contains RGB and multispectral orthophotos with coordinates of approximately 14,000 individual cauliflower plants. The coordinates enable the extraction of complete and incomplete time‐series of image patches showing individual plants. The dataset contains the collected phenotypic traits of 740 plants, including the developmental stage and plant and cauliflower size. The harvestable product is completely covered by leaves, thus, plant IDs and coordinates are provided to extract image pairs of plants pre‐ and post‐defoliation. In addition, to facilitate classification, detection, segmentation, instance segmentation, and other similar computer vision tasks, the proposed dataset contains pixel‐accurate leaf and plant instance segmentations, as well as stem annotations. The proposed dataset was created to facilitate the development and evaluation of various machine‐learning approaches. It focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to advance automation in agriculture. Two baseline results of instance segmentation tasks at the plant and leaf level based on labeled instance segmentation data are presented. The complete GrowliFlower dataset is publicly available (http://rs.ipb.uni-bonn.de/data/growliflower/). [ABSTRACT FROM AUTHOR]

Chlorophyll is one of the most important pigments in plants, and the measurement of chlorophyll levels enables real-time monitoring of plant growth, which is of great importance to the vegetation monitoring. Compared with the high cost and time-consuming operation of hyperspectral imaging technique, the spectral reflectance reconstruction technique based on RGB images has the advantages of being inexpensive and fast. In this study, using the example of ginkgo leaves, the spectra were reconstructed from red-green-blue (RGB) images taken by smartphones based on a back propagation (BP) neural network and pseudo-inverse method. Based on a BP neural network, the maximum absolute error between the reconstructed spectra and the reference spectra acquired by the hyperspectral camera was less than 0.038. A partial least squares regression (PLSR) prediction model for chlorophyll content estimation was established using the reconstructed spectra. The R2 and root mean square error (RMSE) of the validation set were 0.8237 and 1.1895%, respectively, there was a high correlation between predicted and measured values. Compared with the pseudo-inverse method, the maximum absolute error of the reconstructed spectra was reduced by 10.9%, the R2 in the chlorophyll prediction results was improved by 12.7%, and the RMSE was reduced by 19.3%. This research showed that reconstructing spectral reflectance based on RGB images can realize real-time measurement of chlorophyll content. It provided a reliable tool for fast and low-cost monitoring of plant physiology and growth conditions. Graphical Abstract [ABSTRACT FROM AUTHOR]

Climate change and human infrastructures heavily affect seashore dynamics with cascading consequences on coastal sand dunes. While there is a high number of studies conducted on plant communities, there is a lack of monitoring approaches conducted at population level. We studied the variations in spatial patterns of the population of three plant species in response to interannual coastline variation in a coastal Nature Reserve. We performed eight diachronic field observations over the course of eleven years, in which we registered the coastline variation and the position of each functional individual of the selected species. The population of the alien-naturalized and highly clonal Sporobolus pumilus demonstrated the ability to follow the interannual variations of the coastline, thus maintaining a constant distance from the seashore. In contrast, the native Polygonum maritimum and the dune-builder Thinopyrum junceum showed a progressive reduction of the distance from the seashore (− 21 m and − 16 m, respectively). However, this effect was detected only at a finer spatial scale, thus revealing the scale-dependence of this phenomenon. In addition, the spatial distribution patterns of T. junceum changed with the variation and stability of the seashore, showing spatial dispersion and aggregation patterns, respectively. Our study reveals how the plant response to the coastline variation was species-specific, probably depending on species functional strategies. Besides highlighting the importance of frequent diachronic surveys, our population-level approach may help in explaining the compositional changes observed at higher organizational level. Finally, our findings indicate that coastal erosion poses a serious threat for the conservation of dune ecosystems. [ABSTRACT FROM AUTHOR]

[Display omitted] • DeepD381v4plus integrations were discussed to address task-specific challenges. • Although the symptoms of the diseases are similar, they can still be distinguished. • It can classify diseases even with < 5 % or less disease occupancy in the image. • It provides accurate detection and counting in indoor artificial light environments. • It offers real-time optimised farm management solutions. Real-time monitoring of tomato plants in plant factories is necessary to identify and classify diseases at early stages to prevent possible outbreaks. The proposed DeepD381v4plus network exhibits higher class-wise accuracy, sensitivity, specificity, precision, F1 score and Matthews correlation coefficient scores exceeding 0.96 for multi-varietal tomato leaf diseases. During the reproductive stage, bud formation, flower appearance, bite marks and fruit set also need to be monitored to confirm pollination. The detector DeepDet381v4 – YOLOv4M achieves the highest mean average precision (mAP) (0.90) and lowest mAP (0.68) in the TFl_Blooming class and the lowest mAP (0.68) in the TFl_Transforming class. However, in real-world simulations, DeepDet381v4 – YOLOv4M can detect and count ripe tomatoes at a distance of 40 cm with little to no error. Both networks used for classification and detection–counting tasks have small sizes with high classification and detection efficiency (>27 fps). Overall, the proposed experimental approach will help farmers prevent disease outbreaks, monitor flower shapes that can set fruits at the highest rate, timely detect and count ripened fruits or recognise damaged fruits due to surface cracks or diseases for harvesting at their optimal maturity stage. This will reduce the labour costs, improve cultivation management and ensure excellent quality of the harvested tomatoes. [ABSTRACT FROM AUTHOR]

Plant leaf temperature and its environmental parameters provide valuable information on plant growth. This paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition). The developed SCADA system monitors the leaf temperature and the air parameters of temperature and humidity, as well as the soil parameters of temperature, moisture, pH, electrical conductivity, nitrogen, phosphorous, and potassium. A novel method is proposed for measuring the leaf temperature using a low-cost 8 × 8 array thermal camera. The sensor systems in the field are developed to wirelessly communicate with the Hawell IoT Cloud HMI via a Modbus TCP protocol. To visualize the thermal image on the HMI dashboard, a novel approach is proposed wherein the data are transferred using the Modbus TCP protocol. The HMI is connected to a cloud server and can be accessed by the users using the web browser or mobile application on a smartphone. The experimental results show that the proposed hardware, software, and communication protocol are reliable for real-time and continuous plant monitoring. Further, the evaluation of sensor data shows that the data from the thermal camera and air parameters sensor can be independently interpreted. However, the data from the soil sensor should be interpreted in consideration of the other parameters.

The existing models of industrial robots cannot perform technological processes of apple harvesting. It is noted that there is a need for developing special actuators, grippers and new control algorithms for harvesting horticulture products. (Research purpose) The research aimed to develop an intelligent control system for horticulture industrial technologies and robotic techniques for yield monitoring and fruit harvesting. (Materials and methods) The research methodology was based on such modern methods as computer modeling and programming. In particular, the following methods were applied: systems analysis, artificial neural networks theory, pattern recognition, digital signal processing. The development of software, hardware and software was carried out in accordance with the requirements of GOST technical standards. The following programming languages were used: (C / C ++)-based OpenCV library, Spyder Python Development Environment, PyTorch and Flask frameworks, and JavaScript. Image marking for training neural networks was carried out via VGG ImageAnnotator and in Labelbox. The design process was based on the finite element method, CAD SolidWorks software environment. (Results and discussion) An intelligent management system for horticulture industrial technologies has been created based the on the «Agrointellect VIM» hardware and software complex. The concept of the system is shown to be implemented via computer and communication technology, robotic machines, the software for collecting, organizing, analyzing and storing data. The gripper proves to fix an apple gently and holds it securely. Depending on the size, the fruit fixation time is 1.5-2.0 seconds, the fruit maximum size is 85 per 80 millimeters , and its maximum weight is 500 grams. (Conclusions) The developed intelligent control system for industrial technologies based on «Agrointellect VIM» hardware and software complex ensures the efficient real-time processing of information necessary for the design of intelligent agricultural technologies using robotic machines and artificial intelligence systems.

Potassium and sodium ions (K + and Na + ) play crucial roles in influencing plant growth and health status. Unfortunately, current strategies to determine the concentrations of such ions are destructive for the plants because it is necessary to collect/extract the sap for further analysis and produce either scattered or delayed results. Here, we introduce a new potentiometric dual microneedle sensor for nondestructive, real-time, and continuous monitoring of K + and Na + concentrations in living plants. The developed sensors show a response time <5 s, close-to-Nernstian slope (∼55 mV dec -1 ), resiliency to five insertions on the stem, good repeatability (max. %RSD = 0.3%) and reversibility (max. %RSD = 3%), appropriate continuous operation for 24 h, and linear range of responses that cover expected plant physiological levels (5-50 mM for Na + and 50-120 mM for K + ). Moreover, the accuracy was successfully investigated by comparing the results provided by the microneedle sensors to those obtained by a standard reference method (e.g., ion chromatography). Finally, we demonstrate that the developed analytical device is capable of tracking K + and Na + transportation from the hydroponic solution to the stem within 5-10 min. This research will contribute to establishing a new generation of analytical platforms for smart agriculture offering real-time information.

In vivo measurements of the optical properties of plant leaves, by spectrophotometric methods, could potentially provide very useful information for the assessment of the plant’s health. This paper describes a novel experimental set-up that enables continuous measurements of the optical reflection and transmission coefficients of broad-leaved plants. The optical spectral characteristics of leaves, in the broad wavelength range, are often a discussed topic in the scientific literature. Nevertheless, to the best of our knowledge, the real-time spectral analyses of the leaf haven’t been reported so far. Monitoring the evolution of the plant activity in real-time has resulted in the graph of the spectral Circadian rhythm as a function of time. The set-up was tested on Phaseolus vulgaris and the results of the measurements are presented and discussed.

The chlorophyll content has a direct effect on photosynthesis of crops. In order to explore a quick and convenient method for estimating the chlorophyll content of Brassica napus and facilitate efficient crop monitoring, we measured the actual value of chlorophyll with a SPAD-502 chlorophyll detector, and collected aerial images of B. napus with an unmanned aerial vehicle (UAV) carrying a RGB camera in this study. The total number of 270 samples collected images were divided into regions according to the planting conditions of different B. napus varieties in the field. Then, according to the empirical formula, there were 36 colors' characteristic parameters calculated and combined. To estimate the chlorophyll content of rape, 189 samples were included in the modeling set, while the other 81 samples were enrolled in the validation set for testing the accuracy of this model. After the combination of R (red), G (green) and B (blue) color channels, the results showed that the color characteristics B/(R þ G), b, B/G, (G-B)/(G þ B), g-b were highly connected with the measured value of chlorophyll SPAD, and the correlation coefficient between the combination based on B/(R þ G) and SPAD value was 0.747. With R2 - 0.805, RMSE - 3.343, and RE - 6.84%, the regression model created using random forest had superior outcomes, according to the model comparison. This study offers a new method for quickly estimating the amount of chlorophyll in rapeseed and a workable reference for crop monitoring using the UAV platform. [ABSTRACT FROM AUTHOR]

Monitoring canopy water content in alpine scrub is crucial for hydrological and ecological studies in the Qilian Mountains. However, traditional measurement methods are time-consuming and inconvenient to implement due to the harsh climate and lack of transportation in alpine regions. In our study, a new method was proposed to estimate the canopy water content of alpine shrub using spectral data. The water content data for samples with alpine scrub canopy reflectance spectra in the range of 350–2500 nm was collected. Then, a lightweight Transformer regression model was built based on three different feature selection methods (the successive projections algorithm, genetic algorithm, and principal component analysis) to predict the canopy water content of shrubs. The proposed model was compared with three traditional machine learning models: random forest, support vector regression, and back-propagation neural network. The results reveal that the lightweight Transformer model demonstrates strong fitting and generalization capabilities in using only full-band spectra as input, which may be attributed to Transformer's own self-attention mechanism. From cross-validation, the RMSE and R2 of the model on the training (validation) set are 0.0201 (0.0233) and 0.968 (0.965), respectively. Furthermore, the performance of all regression models is enhanced after feature selection. On the validation set, PCA-Transformer has the smallest RMSE (0.0147) and largest R2 (0.985), and its running efficiency is improved by 81.69 % relative to the full-band spectra as input. In the additional test set, the predictive ability of the models remain stable. SPA-Transformer has the smallest RMSE (0.0111) and largest R2 (0.986). These suggest the excellent performance of lightweight Transformer on the task of estimating scrub water content and the ability of feature selection methods to optimize models. The results show that PCA-Transformer is an accurative and effictive tool to estimate plant water content, and offer a foundation for monitoring large-scale canopy water content in alpine regions using remote sensing technology. [ABSTRACT FROM AUTHOR]

Background: Studying dynamic processes in living organisms with MRI is one of the most promising research areas. The use of paramagnetic compounds as contrast agents (CA), has proven key to such studies, but so far, the lack of appropriate techniques limits the application of CA-technologies in experimental plant biology. The presented proof-of-principle aims to support method and knowledge transfer from medical research to plant science. Results: In this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI). The new approach has been applied in situ to a cereal crop (Hordeum vulgare). The pDCE-MRI allows non-invasive investigation of CA allocation within plant tissues. In our experiments, gadolinium-DTPA, the most commonly used contrast agent in medical MRI, was employed. By acquiring dynamic T1-maps, a new approach visualizes an alteration of a tissue-specific MRI parameter T1 (longitudinal relaxation time) in response to the CA. Both, the measurement of local CA concentration and the monitoring of translocation in low velocity ranges (cm/h) was possible using this CA-enhanced method. Conclusions: A novel pDCE-MRI method is presented for non-invasive investigation of paramagnetic CA allocation in living plants. The temporal resolution of the T1-mapping has been significantly improved to enable the dynamic in vivo analysis of transport processes at low-velocity ranges, which are common in plants. The newly developed procedure allows to identify vascular regions and to estimate their involvement in CA allocation. Therefore, the presented technique opens a perspective for further development of CA-aided MRI experiments in plant biology. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) realizes the real-time video monitoring of plant propagation or growth in the wild. However, the monitoring time is seriously limited by the battery capacity of the visual sensor, which poses a challenge to the long-working plant monitoring. Video coding is the most consuming component in a visual sensor, it is important to design an energy-efficient video codec in order to extend the time of monitoring plants. This article presents an energy-efficient Compressive Video Sensing (CVS) system to make the visual sensor green. We fuse a context-based allocation into CVS to improve the reconstruction quality with fewer computations. Especially, considering the practicality of CVS, we extract the contexts of video frames from compressive measurements but not from original pixels. Adapting to these contexts, more measurements are allocated to capture the complex structures but fewer to the simple structures. This adaptive allocation enables the low-complexity recovery algorithm to produce high-quality reconstructed video sequences. Experimental results show that by deploying the proposed context-based CVS system on the visual sensor, the rate-distortion performance is significantly improved when comparing it with some state-of-the-art methods, and the computational complexity is also reduced, resulting in a low energy consumption. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) realizes the real-time video monitoring of plant propagation or growth in the wild. However, the monitoring time is seriously limited by the battery capacity of the visual sensor, which poses a challenge to the long-working plant monitoring. Video coding is the most consuming component in a visual sensor, it is important to design an energy-efficient video codec in order to extend the time of monitoring plants. This article presents an energy-efficient Compressive Video Sensing (CVS) system to make the visual sensor green. We fuse a context-based allocation into CVS to improve the reconstruction quality with fewer computations. Especially, considering the practicality of CVS, we extract the contexts of video frames from compressive measurements but not from original pixels. Adapting to these contexts, more measurements are allocated to capture the complex structures but fewer to the simple structures. This adaptive allocation enables the low-complexity recovery algorithm to produce high-quality reconstructed video sequences. Experimental results show that by deploying the proposed context-based CVS system on the visual sensor, the rate-distortion performance is significantly improved when comparing it with some state-of-the-art methods, and the computational complexity is also reduced, resulting in a low energy consumption.

Well-balanced nutrition is important for the successful cultivation of healthy plants. In this paper, we demonstrate a nondestructive optical method that can sense a deficiency of certain nutrients. The setup was tested on hydroponically grown Ocimum basilicum. The plants were subjected to nutrient deficiency by the exclusion of one of the essential elements (Fe, Mg, P, N) from the hydroponic solution. A control group of plants, fed by the balanced hydroponic solution, was also grown under the same conditions. The proposed method tracks and records the optical transmittance of the plants' leaves. All groups exhibit clearly defined day-night Circadian rhythms. When compared to the control group, the treated plants exhibited modified circadian rhythms of the optical transmission, suggesting an early indicator of the plants' stress. The condition of the plants under test was also assessed by the more common (destructive) methods such as: measurements of the determination of the photosynthetic pigment content, dray weight determination and the efficiency of PSII. Several biological parameters were observed, calculated and compared to the graphs of optical transmission dependence in real time. Presented results have demonstrated a significant potential of the proposed optical method for the early detection of plants' stress in hydroponic cultivation. [ABSTRACT FROM AUTHOR]

Potted plants have been reported to uptake VOCs and help "cleaning" the air. This paper presents the results of a laboratory study in which two species of plants (peace lily and Boston fern) and three kinds of substrates (expanded clay, soil, and activated carbon) were tested and monitored on their capacity to deplete formaldehyde and CO2 in a glass chamber. Formaldehyde and CO2 were selected as indicators to evaluate the biofiltration efficacy of 28 different test conditions; relative humidity (RH) and temperature (T) were monitored during the experiments. To evaluate the efficacy of every test, the clean air delivery rate (CADR) was calculated. Overall, soil had the best performance in removing formaldehyde (~0.07–0.16 m3/h), while plants, in particular, were more effective in reducing CO2 concentrations (peace lily 0.01m3/h) (Boston fern 0.02–0.03 m3/h). On average, plants (~0.03 m3/h) were as effective as dry expanded clay (0.02–0.04 m3/h) in depleting formaldehyde from the chamber. Regarding air-cleaning performance, Boston ferns presented the best performance among the plant species, and the best performing substrate was the soil. [ABSTRACT FROM AUTHOR]

In vivo measurements of the optical properties of plant leaves, by spectrophotometric methods, could potentially provide very useful information for the assessment of the plant's health. This paper describes a novel experimental set-up that enables continuous measurements of the optical reflection and transmission coefficients of broad-leaved plants. The optical spectral characteristics of leaves, in the broad wavelength range, are often a discussed topic in the scientific literature. Nevertheless, to the best of our knowledge, the real-time spectral analyses of the leaf haven't been reported so far. Monitoring the evolution of the plant activity in real-time has resulted in the graph of the spectral Circadian rhythm as a function of time. The set-up was tested on Phaseolus vulgaris and the results of the measurements are presented and discussed. [ABSTRACT FROM AUTHOR]

In a large-scale plant such as a nuclear power plant, thousands of process values are measured for the purpose of monitoring the plant performance and the health of various systems. It is difficult for plant operators to constantly monitor all of the process values. We present a data-driven method to comprehensively monitor a large number of process values and detect early signs of anomalies, including unknown events, with few false positives. In order to learn the complex changing internal state of a nuclear power plant and accurately predict the normal process values, we created a two-stage autoencoder composed of a time window autoencoder and a deviation autoencoder, which is a deep learning network structure corresponding to the characteristics of the process values. We assessed performances of the two-stage autoencoder with simulated process values of a nuclear power plant, a 1,100 MW boiling water reactor having 3,100 analog process values. In situations where it is difficult to predict the normal state (rapid operation mode change, transient state, and small fluctuations in the process values), the two-stage autoencoder properly predicted the normal process values and showed excellent performances with early detection and zero false positives, except for one case. The two-stage autoencoder would be an effective solution for comprehensive plant monitoring and early detection of anomaly signs.

In this work, basil plants were fertilized with 0, 2.5 mM and 10 mM nitrogen (with different NO3−/NH4+ ratios), and then monitored using a low-power technique based on an optical leaf meter and a low-cost RGB sensor interfaced with an Arduino UNO board. The study aimed to investigate possible relationships between the concentration of some plant compounds (i.e., leaf chlorophyll and flavonoids content) and the nitrogen balance index, with the output data of a low-cost RGB sensor to indicate its capability in discriminating among different levels of nutrition. The data obtained underwent univariate and multivariate analysis. The univariate data analysis showed that the low-cost RGB sensor readings followed the development of the plants according to the varying applications of nitrogen. The multivariate analysis of the data showed that the indices related to plant metabolic efficiency and leaf colour were those most affected by the nitrogen levels of the solutions used. The comparison of the discrimination powers of the systems showed that both systems achieved comparable discrimination performances (85.0% and 89.4%) for plants supplied with 0 mM nitrogen solution. However, at increasing levels of nitrogen, the RGB sensor performed worse than the optical leaf meter (− 15.8% and − 8.6% for the 2.5 and 10 mM N treatments). The effect of the NO3−/NH4+ ratio could hardly be distinguished (except for the total chlorophyll resulting from the optical leaf meter readings). More data is, however, necessary to create a more robust model for future implementation of the application of such a sensor. [ABSTRACT FROM AUTHOR]

Pl@ntnet is a citizen observatory that relies on artificial intelligence (AI) technologies to help people identify plants with their smartphones (Joly 2014). Over the past few years, Pl@ntNet has become one of the largest plant biodiversity observatories in the world with several million contributors (Bonnet 2020b). Based on user demands, a set of tools and services following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson 2016) were implemented to allow the development of e-floras. After a short description of the platform (see Joly (2015) and Joly (2016) for details), we present three complementary services dedicated to the customization of e-floras. The general workflow of Pl@ntNet can be divided into three main components. First, the Pl@ntNet mobile Android and iOS apps, dedicated to plant identification, allow both anonymous and authenticated users to take a picture of a plant, and to send it to a server for recognition at the species level. This recognition, which is performed by a convolutional neural network (Affouard 2017), allows the user to get a list of candidate species, each associated with a confidence score. The second component of Pl@ntNet's workflow is the enrichment of the database to improve the observation quality of the data collected. Because of the huge volume of data (over four million observations shared in 2020, Pl@nt Net statistics), this enrichment cannot be done solely by expert botanists. This is why various crowdsourcing mechanisms have been put in place allowing the collaborative curation of the data. The third part of the Pl@ntNet workflow is the exploitation of the database, which is made available for the needs of stakeholders such as researchers. For example, the recent sharing of Pl@ntNet data on GBIF's (Global Biodiversity Information Facility) platform, through two complementary datasets, has increased the benefit to the scientific community (examples are available here). To support the development of customized e-floras, three complementary concepts have been developed: micro-projects, groups, and monitoring work spaces, whose services are detailed below: (a) Micro-projects allow full adaptation of all the interfaces of the Pl@ntNet apps to a species list of interest. This e-flora can be linked to a specific geographical area, which allows it to be automatically selected according to the user's location. When several specific geographical areas overlap for a given geolocation, the smallest one is automatically selected. This adaptation increases the accuracy of the identification, as the number of potential species for a given identification request is reduced to the checklist of the micro-project (e.g., Bonnet (2020a)). Up to now, 12 micro-projects adapted to European, African or Asian contexts are running on the platform. A full description of this service is provided in Bonnet (2020c). The API (Application Programming Interface) for the identification service of each e-flora is available on My-Pl@ntNet. (b) Groups allow any user to create a private or public space on the platform (https://identify.plantnet.org/groups), to permit everyone to aggregate a part or all of their observations in the group. A group is "observation-centered" as opposed to a micro-project, which is species-centered. If the group is public, any authenticated user can join and contribute to it; if it is private, only users validated by the group's moderators can become members. When a group is restricted to a specific geographical zone (such as a school, city, or natural area), only observations found in that area are displayed in the group, contrary the micro-project. As all the group's observations can be downloaded (as tabbed or comma separated values) by any of the group's members, group features can be used to conduct statistical analyses on the data in order to study plant plots, plant phenology or user profiles. These groups are used by people who want to structure the activity of a group of people interested in monitoring the biodiversity of a given area, a taxonomical group, or a type of plant habitat. Over 260 groups have already been created by e.g., professional land managers, educators, and plant enthusiasts. (c) Monitoring work spaces allows a given stakeholder to access all the observations and identification requests of a given species list in a particular area. Micro-projects and Groups only allow exploration of plant observations explicitly shared by the authenticated users. However, Pl@ntNet's database contains hundreds of millions of plant identification requests submitted by anonymous users. Monitoring work spaces was set up to allow access by land managers to this rich and important material. These work spaces provide the maps, the list of plant observations, and identification requests (with a very high confidence score on the species identification), for all the species of interest to a given partner. For example, this service has been mobilized to follow the recent development of an invasive species (i.e., Hakea sericea Schrad. & J.C. Wendl.) in and around a natural reserve on the Mediterranean coast. All of these on-demand e-floras and monitoring services accelerate the use of dailyproduced data, and inform land managers and scientists of the changes in the floristic composition of monitoring areas. [ABSTRACT FROM AUTHOR]

Potted plants have been reported to uptake VOCs and help “cleaning” the air. This paper presents the results of a laboratory study in which two species of plants (peace lily and Boston fern) and three kinds of substrates (expanded clay, soil, and activated carbon) were tested and monitored on their capacity to deplete formaldehyde and CO2 in a glass chamber. Formaldehyde and CO2 were selected as indicators to evaluate the biofiltration efficacy of 28 different test conditions; relative humidity (RH) and temperature (T) were monitored during the experiments. To evaluate the efficacy of every test, the clean air delivery rate (CADR) was calculated. Overall, soil had the best performance in removing formaldehyde (~0.07–0.16 m3/h), while plants, in particular, were more effective in reducing CO2 concentrations (peace lily 0.01m3/h) (Boston fern 0.02–0.03 m3/h). On average, plants (~0.03 m3/h) were as effective as dry expanded clay (0.02–0.04 m3/h) in depleting formaldehyde from the chamber. Regarding air-cleaning performance, Boston ferns presented the best performance among the plant species, and the best performing substrate was the soil.

Introduction. Fine dust puts human health at risk in populated areas. The research is focused on dust particles on the leaves of apricot trees (Prúnus armeníaca) in the residential area of the Central Akhtuba district of the Volgograd region (the area of single family houses and low rise townhouses) and in the public area (the area of public amenities) located in close proximity to construction and development enterprises, machine builders, etc. as compared to the dispersion composition of dust on the leaves of apricot trees (Prúnus armeníaca) in the territory occupied by rural housing communities in the conditionally clean zone (the benchmark zone) in absence of any man-induced loadings coming from industrial facilities (Oroshenets gardeners’ non-commercial partnership, Volgograd, Soviet district). The goal of the research is to study the dispersion composition of dust and to identify fine dust PM2.5 and PM10 using plant monitoring systems in the residential area. The objectives include the sampling of dust particles from plant leaves, dust analysis, and identification of areas exposed to ecological risks. Materials and methods. The leaves of apricot trees (Prúnus armeníaca), growing in residential and public areas of the Central Akhtuba district of the Volgograd region and in the territory of Oroshenets gardeners’ non-commercial partnership, Volgograd, Soviet district (the conditionally clean benchmark zone) represent the study material. Methods of research include leaf wash into the beaker full of distilled water, suspension filtration using AFA-VP analytical filter, filtrate production and natural drying at the temperature not exceeding 30–40 °C, and placement of the dried filtrate on the slide of an optical microscope. The distribution of dust particles was performed pursuant to State Standard of Russia 56929-2016. Results. The co-authors have identified the zones exposed to ecological risks in residential and public areas of the Central Akhtuba district of the Volgograd region (Bolnichnaya st., residential houses in Nechayeva, Omskaya streets, a kindergarten in Kavkazskaya st.: PM2.5, PM10) and safe residential areas (a boarding school in Vorovskogo st.: PM10 — 20 μm and 20–40 μm). Conclusions. Further studies of residential and public areas of the Central Akhtuba district of the Volgograd region are to be performed to identify the chemical composition of dust particles and the source of fine dust polluting the air and to develop a roadmap of environmental actions. [ABSTRACT FROM AUTHOR]

Presence–absence sampling is an important method for monitoring state and change of both individual plant species and communities. With this method, only the presence or absence of the target species is recorded on plots and thus the method is straightforward to apply and less prone to surveyor judgement compared to other vegetation monitoring methods. However, in the basic setting, all plots must be equally large or otherwise it is unclear how data should be analysed. In this study, we propose and evaluate five different methods for estimating plant density based on presence–absence registrations from surveys with variable plot sizes.Using artificial plant population data as well as empirical data from the Swedish National Forest Inventory, we evaluated the performance of the proposed methods. The main analysis was conducted through sampling simulation in artificial populations, whereby bias and variance of density estimators for the different methods were quantified and compared.Both for state and change estimation of plant density, we found that the best method to handle variable plot size was to perform generalized least squares regression, using plot size as an independent variable. Methods where plots smaller than a certain threshold were excluded or their registrations recalculated were, however, almost as good. Using all registrations as if they were obtained from plots with the nominal plot size resulted in substantial bias.Our findings are important for plant population studies in a wide range of environmental monitoring programmes. In these programmes, plots are typically randomly laid out and may be located across boundaries between different land‐use or land‐cover classes, resulting in subplots of variable size. Such splitting of plots is common when large plots are used, for example, with the 100 m2 plots used in the Swedish National Forest Inventory. Our methods overcome problems to estimate plant density from presence–absence data observed in plots that vary in size. [ABSTRACT FROM AUTHOR]

Inventories of plant populations are fundamental in ecological research and monitoring, but such surveys are often prone to field assessment errors. Presence/absence (P/A) sampling may have advantages over plant cover assessments for reducing such errors. However, the linking between P/A data and plant density depends on model assumptions for plant spatial distributions. Previous studies have shown, for example, how that plant density can be estimated under Poisson model assumptions on the plant locations. In this study, new methods are developed and evaluated for linking P/A data with plant density assuming that plants occur in clustered spatial patterns.New theory was derived for estimating plant density under Neyman–Scott‐type cluster models such as the Matérn and Thomas cluster processes. Suggested estimators, corresponding confidence intervals and a proposed goodness‐of‐fit test were evaluated in a Monte Carlo simulation study assuming a Matérn cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application.The simulation study showed that our methods work well for large enough sample sizes. The judgment of what is' large enough' is often difficult, but simulations indicate that a sample size is large enough when the sampling distributions of the parameter estimators are symmetric or mildly skewed. Bootstrap may be used to check whether this is true. The empirical results suggest that the derived methodology may be useful for estimating density of plants such as Leucanthemum vulgare and Scorzonera humilis.By developing estimators of plant density from P/A data under realistic model assumptions about plants' spatial distributions, P/A sampling will become a more useful tool for inventories of plant populations. Our new theory is an important step in this direction. [ABSTRACT FROM AUTHOR]

Double sampling (DS) s charts are designed to allow quick detection of a small shift in process standard deviations and to provide a quick response in an agile manufacturing environment. However, current developed DS s charts assume that the sample standard deviations follow a normal distribution. Although valid for relatively large sample sizes, this assumption has limited the application of DS s charts to the monitoring of manufacturing processes with relatively small sample sizes. An improved DS s chart is developed without the normality assumption of the sample standard deviations. The design of the improved DS s chart is formulated as a statistical design optimization problem and solved with a genetic algorithm. The efficiency of the improved DS s charts is compared with that of the DS s charts developed in previous research and with that of the traditional s charts. [ABSTRACT FROM AUTHOR]

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.

In condition-based maintenance, a common practice is to record a condition reading at a regular interval, and once the reading is higher than a pre-set critical level, the item monitored is declared faulty and repair or replacement may be initiated. However, surprisingly both in practice and theory, little attention has been paid to whether or not the critical level and the monitoring interval are set in a cost effective way. This paper reports on the development of a model that can be used to determine the optimal critical level and interval in condition-based maintenance in terms of a criterion of interest. The model is established on the basis of the random coefficient growth model where the coefficients of the regression growth model are assumed to follow known distribution functions. A simple example is given in the paper to illustrate the modelling ideas. [ABSTRACT FROM AUTHOR]

Abstract Legacy databases provide unique information on soil properties and act as a guide for the setup of monitoring processes. However, their use requires an evaluation of their drawbacks, especially when aiming to model the soil traits by depth. We set up a procedure for the integration and error correction of a soil legacy database. This database consisted of 6994 records in its original form and 6674 records after correction. These records were collected from 2886 locations in the south of Italy on a 25,711-km2 island (Sicily, Italy). Samples were taken in arable lands (5471 records), orchards, vineyards and seminatural lands (3010 records), and woodland and natural areas (1203 records). The procedure for the integration and error highlighting improved the prediction of soil organic carbon (SOC), and a general linear model with covariate selection by Least Absolute Shrinkage and Selection Operator (LASSO) tested the procedure. We focussed on exploring the amount of legacy information as georeferenced soil properties. SOC and fine earth fractions were analysed for each sample. Bulk density was provided for only 20% of the samples. These results will help to account for the legacy data available and propose an analysis to harmonize an SOC dataset; highlight missing or incorrect data; summarize data; and offer synthesis criteria for benchmarking SOC in different land uses and pedological areas. In addition, the results may stimulate funding bodies to support research in an open data frame, which can be turned into more sustainable use of resources, improved communication between governments and farmers, and the production of standard datasets that meet and facilitate the requirements for regional agro-environmental modelling. Highlights • SOC legacy database analysis, integration and correction by data clearing • The correction can drive data expansion without taking redundant data. • 4.5% uninformative records removed • Correction fosters the use of legacy soil data for Space 3D-time soil mapping [ABSTRACT FROM AUTHOR]