Agricultural Monitoring Applications Research Articles

Evaluating Bacterial Nanocellulose Interfaces for Recording Surface Biopotentials from Plants.

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC-carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings.

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis.

With the rapid advancement of remote sensing technology is that the need for efficient and accurate crop classification methods has become increasingly important. This is due to the ever-growing demand for food security and environmental monitoring. Traditional crop classification methods have limitations in terms of accuracy and scalability, especially when dealing with large datasets of high-resolution remote sensing images. This study aims to develop a novel crop classification technique, named Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC) for analyzing remote sensing images. The objective is to achieve high classification accuracy for various food crops. The proposed DTODCNN-CC approach consists of the following key components. Deep convolutional neural network (DCNN) a GoogleNet architecture is employed to extract robust feature vectors from the remote sensing images. The Dipper throated optimization (DTO) optimizer is used for hyper parameter tuning of the GoogleNet model to achieve optimal feature extraction performance. Extreme Learning Machine (ELM): This machine learning algorithm is utilized for the classification of different food crops based on the extracted features. The modified sine cosine algorithm (MSCA) optimization technique is used to fine-tune the parameters of ELM for improved classification accuracy. Extensive experimental analyses are conducted to evaluate the performance of the proposed DTODCNN-CC approach. The results demonstrate that DTODCNN-CC can achieve significantly higher crop classification accuracy compared to other state-of-the-art deep learning methods. The proposed DTODCNN-CC technique provides a promising solution for efficient and accurate crop classification using remote sensing images. This approach has the potential to be a valuable tool for various applications in agriculture, food security, and environmental monitoring.

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing.

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

Maximizing Energy Efficiency of Period-Area Coverage With a UAV for Wireless Rechargeable Sensor Networks

Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like oceanic monitoring and precision agriculture. Rechargeable sensors, together with an Unmanned Aerial Vehicle (UAV), are collaboratively employed for fulfilling periodic coverage missions. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such novel coverage requirements. In this paper, we propose the concept of Period-Area Coverage (PAC) problem, which requires the data of the overall area must be collected/monitored periodically. To solve the PAC problem, we employ a UAV that simultaneously acts as a mobile charger and sensor. It is responsible for charging nearly exhausted sensors and sensing vacant regions to realize complete event monitoring. To maximize the energy efficiency of the UAV, we propose a heuristic hexagon-based scheduling algorithm (HSA) which can also balance energy consumption. Furthermore, we develop an emergent node charging scheduling method to prevent node exhaustion, and introduce a grid-based boustrophedon scheduling algorithm (GBSA) to reduce the complexity. Finally, we present a charging re-allocation mechanism to further enhance energy efficiency. Extensive simulations demonstrate that the proposed schemes can solve the PAC problem and enhance energy efficiency by at least <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <inline-formula> <tex-math notation="LaTeX">$18.2\%$</tex-math> </inline-formula> </b> compared to prior arts. Test-bed experiments conducted both in agriculture and oceanic monitoring applications validate the applicability of the proposed scheme in practical scenarios.

In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large amount of high spatiotemporal resolution remote sensing data, different data sources are expected to increase the frequency of data acquisition, which can provide more information in the early season. To explore the potential of integrating different data sources, a Dual-1DCNN algorithm was built based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtain the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2) attained in 2019. To verify this method, the classical methods support vector machine (SVM), random forest (RF), and Mono-1DCNN were implemented. The input for SVM and RF was S1A and S2 data, and the input for Mono-1DCNN was S2 data. The results demonstrated the following: (1) Dual-1DCNN achieved an overall accuracy above 85% at the earliest time.; (2) all four types of models achieved high accuracy (F1s were greater than 90%) on summer maize after sowing one month later; (3) for cotton and common yam rhizomes, Dual-1DCNN performed best, with its F1 reaching 85% within 2 months after cotton sowing, 15 days, 20 days, and 45 days ahead of Mono-1DCNN, SVM, and RF, respectively, and its extraction of the common yam rhizome was achieved 1–2 months earlier than other methods within the acceptable accuracy. These results confirmed that Dual-1DCNN offered significant potential in the in-season detection of crop types.

Crop Type Maps for Operational Global Agricultural Monitoring

Crop type maps identify the spatial distribution of crop types and underpin a large range of agricultural monitoring applications ranging from early warning of crop shortfalls, crop condition assessments, production forecasts, and damage assessment from extreme weather, to agricultural statistics, agricultural insurance, and climate mitigation and adaptation decisions. Despite their importance, harmonized, up-to-date global crop type maps of the main food commodities do not exist to date. To address this critical data gap of global-scale consistent, up-to-date crop type maps, we harmonized 24 national and regional datasets from 21 sources covering 66 countries to develop a set of Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM.

Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS

Accurate detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. To improve the performance of detecting plant leaves in natural scenes containing severe occlusion, overlapping, or shape variation, we developed an in situ sweet potato leaf detection method based on a modified Faster R-CNN framework and visual attention mechanism. First, a convolutional block attention module was added to the backbone network to enhance and extract critical features of leaf images by fusing cross-channel information and spatial information. Subsequently, the DIoU-NMS algorithm was adopted to modify the regional proposal network by replacing the original NMS. DIoU-NMS was utilized to reduce missed and incorrect detection in scenes of densely distributed leaves by considering the targets' overlap ratio, distance, and scale. The proposed leaf detection method was tested and evaluated on sweet potato plant images collected in agricultural fields. In the datasets, sweet potato leaves were presented in various sizes and poses, and a large proportion of leaves were occluded or overlapped with each other. The experimental results showed that the proposed leaf detection method outperforms state-of-the-art object detection methods. The mean average precision of the proposed method reached 95.7%, which was 2.9% higher than that of the original Faster R-CNN and 7.0% higher than that of YOLOv5. The proposed method achieved promising performance in detecting dense leaves or occluded leaves and could provide key techniques for applications in smart agriculture and ecological monitoring, such as growth monitoring or plant phenotyping.

IoTaaS: Drone-Based Internet of Things as a Service Framework for Smart Cities

The Internet of Things (IoT) offers new services in the context of smart cities through digital devices embedded with sensing, computation, and communication capabilities. The IoT devices enhance the smart city vision by employing advanced communication and computation technologies for smart city administrations. The IoT-based smart city applications require many IoT devices and gateways to be deployed at different city points. Heterogeneous sensing devices, placing smart devices in a constrained or physically inaccessible area, and large urban areas to monitor together make IoT node deployment and sensing management tasks difficult, time-consuming, and expensive. Additionally, certain tasks may require smart devices to be deployed for a very short period of time to sense and report contextual information, making it economically infeasible to purchase the devices. In this regard, we propose a drone-based IoT as a Service (IoTaaS) framework that enables the dynamic provisioning or deployment of IoT devices using drones. IoTaaS allows IoT devices and gateways to be mounted on drones and provides a distributed cloud service by placing the IoT devices in an area according to the requirements specified by a user. We also provide an economic analysis for operating such drone-based IoT services. A proof-of-concept implementation of IoTaaS for smart agriculture and air pollution monitoring applications shows that IoTaaS can reduce setup costs and increase the usage of IoT devices.

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics

Access to low-burden molecular diagnostics that can be deployed into the community for testing is increasingly important and has meaningful wider implications for the well-being of societies and economic stability. Recent years have seen several new isothermal diagnostic modalities emerge to meet the need for rapid, low-cost molecular diagnostics. We have contributed to this effort through the development and patient validation of toehold switch-based diagnostics, including diagnostics for the mosquito-borne Zika and chikungunya viruses, which provided performance comparable to gold-standard reverse transcription-quantitative polymerase chain reaction (RT-qPCR) based assays. These diagnostics are inexpensive to develop and manufacture, and they have the potential to provide diagnostic capacity to low-resource environments. Here the protocol provides all the steps necessary for the development of a switch-based assay for Zika virus detection. The article takes readers through the stepwise diagnostic development process. First, genomic sequences of Zika virus serve as inputs for the computational design of candidate switches using open-source software. Next, the assembly of the sensors for empirical screening with synthetic RNA sequences and optimization of diagnostic sensitivity is shown. Once complete, validation is performed with patient samples in parallel with RT-qPCR, and a purpose-built optical reader, PLUM. This work provides a technical roadmap to researchers for the development of low-cost toehold switch-based sensors for applications in human health, agriculture, and environmental monitoring.

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics.

Access to low-burden molecular diagnostics that can be deployed into the community for testing is increasingly important and has meaningful wider implications for the well-being of societies and economic stability. Recent years have seen several new isothermal diagnostic modalities emerge to meet the need for rapid, low-cost molecular diagnostics. We have contributed to this effort through the development and patient validation of toehold switch-based diagnostics, including diagnostics for the mosquito-borne Zika and chikungunya viruses, which provided performance comparable to gold-standard reverse transcription-quantitative polymerase chain reaction (RT-qPCR) based assays. These diagnostics are inexpensive to develop and manufacture, and they have the potential to provide diagnostic capacity to low-resource environments. Here the protocol provides all the steps necessary for the development of a switch-based assay for Zika virus detection. The article takes readers through the stepwise diagnostic development process. First, genomic sequences of Zika virus serve as inputs for the computational design of candidate switches using open-source software. Next, the assembly of the sensors for empirical screening with synthetic RNA sequences and optimization of diagnostic sensitivity is shown. Once complete, validation is performed with patient samples in parallel with RT-qPCR, and a purpose-built optical reader, PLUM. This work provides a technical roadmap to researchers for the development of low-cost toehold switch-based sensors for applications in human health, agriculture, and environmental monitoring.

Direct Absorption and Photoacoustic Spectroscopy for Gas Sensing and Analysis: A Critical Review

AbstractOptical spectroscopy has a broad scientific basis in chemistry, physics, and material science, with diverse applications in medicine, pharmaceuticals, agriculture, and environmental monitoring. Fourier transform infrared (FTIR) spectrometers and tunable laser spectrometers (TLS) are key devices for measuring optical spectra. Superior performance in terms of sensitivity, selectivity, accuracy, and resolution is required for applications in gas sensing. This review deals with gas measurement based on either direct optical absorption spectroscopy or photoacoustic spectroscopy. Both approaches are applicable to FTIR spectroscopy or TLS. In photoacoustic spectroscopy, cantilever‐based photoacoustic spectroscopy is focused due its high performance. A literature survey is conducted revealing the recent technological advances. Theoretical fundamental detection limits are derived for TLS and FTIR, considering both direct absorption and photoacoustic spectroscopies. A theoretical comparison reveals which technology performs better. The minimum normalized absorption coefficient and normalized noise equivalent absorption coefficient appear as key parameters for this comparison. For TLS‐based systems, direct absorption spectroscopy is found to be the best for lower laser power and longer path length. For FTIR‐based systems, direct absorption is found to be the best for low temperature sources, higher spectrometer throughput, faster mirror velocity, and longer gas cells.

Evaluation of LoRa technology in 433-MHz and 868-MHz for underground to aboveground data transmission

• Development of nodes suited for UG2AG data transmission based on LoRa technology. • Evaluation of 433 MHz and 868 MHz radio modules in the regard of European regulations. • Experiments in real conditions on an open field at 15 and 30 cm burial depths. • Improved performance by placing the emitting antenna directly in contact with the soil. • Interest of the inclination and orientation of the receiving antenna. The development of Wireless Underground Sensor Networks (WUSNs) is currently receiving significant attention to collect data underground all along the year without impacting aboveground activities. Although the opportunities are promising for sectors as agriculture and environment monitoring, the task is particularly challenging as the radio waves are significantly more attenuated in the soil in comparison with in the air. In addition, the communication ranges are highly impacted by some operating and environmental conditions as the soil moisture, its composition and compaction as well as the burial depth of the nodes. In this paper, we developed two sets of nodes operating at 433 MHz and 868 MHz based on the LoRa technology which is the physical layer of the Low Power Wide Area Network LoRaWAN and initially developed for aboveground IoT applications. We successively tested these nodes in real conditions on underground to aboveground (UG2AG) data transmissions and with various operating conditions and radio parameters. First results highlighted the interest of the 868 MHz radio modules tuned at the maximal allowed transmit power in Europe (+14 dBm/25 mW), in comparison with the 433 MHz radio modules (+10 dBm/10 mW). Next results enabled to point out the importance of the inclination of the receiving antenna but also the impact of the burial depth of the emitting node, as well as the interest to place the emitting antenna directly in contact with the soil. The best configuration enabled to reach UG2AG ranges of more than 275 meters long with low depth buried nodes (15 to 30 cm), that clearly enables to envision agriculture and environment monitoring applications based on such radio modules.

Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging

Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications.

Improving Sustainable Vegetation Indices Processing on Low-Cost Architectures

The development of embedded systems in sustainable precision agriculture has provided an important benefit in terms of processing time and accuracy of results, which has influenced the revolution in this field of research. This paper presents a study on vegetation monitoring algorithms based on Normalized Green-Red Difference Index (NGRDI) and Visible Atmospherically Resistant Index (VARI) in agricultural areas using embedded systems. These algorithms include processing and pre-processing to increase the accuracy of sustainability monitoring. The proposed algorithm was evaluated on a real database in the Souss Massa region in Morocco. The collection of data was based on unmanned aerial vehicles images hand data using four different agricultural products. The results in terms of processing time have been implemented on several architectures: Desktop, Odroid XU4, Jetson Nano, and Raspberry. However, this paper introduces a thorough study of the Hardware/Software Co-Design approach to choose the most suitable system for our proposed algorithm that responds to the different temporal and architectural constraints. The evaluation proved that we could process 311 frames/s in the case of low resolution, which gives real-time processing for agricultural field monitoring applications. The evaluation of the proposed algorithm on several architectures has shown that the low-cost XU4 card gives the best results in terms of processing time, power consumption, and computation flexibility.

Pixel-Wise Classification of Hyperspectral Images With 1D Convolutional SVM Networks

Nowadays, remote sensing image analysis is needed in various important tasks such as city planning, land-use classification, agriculture monitoring, military surveillance, and many other applications. In this context, hyperspectral images can play a useful role, but require specific handling. This paper presents a convolutional neural network based on one-dimensional support vector machine (SVM) convolution operations (1D-CSVM) for the analysis of hyperspectral images. SVM-based CNN (CSVM) was introduced first for the classification of high spatial resolution RGB images. It relies on linear SVMs to create filter banks in the convolution layers. In this work, the network is modified to cope with one-dimensional hyperspectral signatures and perform pixel-based classification. It thus analyzes each pixel spectrum independently from the pixel spatial neighborhood. Experiments were carried out on four benchmark hyperspectral datasets, Salinas-A, Kennedy Space Center (KSC), Indian Pines (IP) and Pavia University (Pavia-U). Compared to state-of-the-art models, the proposed network produces promising results for all tested datasets, with an accuracy up to 99.76%.

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

In this work, we introduce Sen4AgriNet, a Sentinel-2-based time series multicountry benchmark dataset, tailored for agricultural monitoring applications with machine and deep learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the land parcel identification system (LPIS) for harmonizing country-wide labels. These declarations have only recently been made available as open data, allowing for the first time the labeling of satellite imagery from ground truth data. We proceed to propose and standardize a new crop type taxonomy across Europe that addresses common agriculture policy (CAP) needs, based on the Food and Agriculture Organization (FAO) indicative crop classification scheme. Sen4AgriNet is the only multicountry, multiyear dataset that includes all spectral information. It is constructed to cover the period 2016–2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives. We extract two subdatasets to highlight its value for diverse deep learning applications—the object aggregated dataset (OAD) and the patches assembled dataset (PAD). OAD capitalizes zonal statistics of each parcel, thus creating a powerful label-to-features instance for classification algorithms. On the other hand, PAD structure generalizes the classification problem to parcel extraction and semantic segmentation and labeling. The PAD and OAD are examined under three different scenarios to showcase and model the effects of spatial and temporal variability across different years and different countries. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Analysis of metamaterial‐inspired soil moisture microwave sensor

AbstractIn this letter, the authors propose a microwave sensor capable to detect soil moisture content parameter for use in precision agriculture and agricultural smart monitoring applications. The novel sensor is based on complementary split‐ring resonator metamaterial topology, microstrip line and was built under biodegradable dielectric substrate of polylactic acid for sustainable purposes. The sensing mechanism relates the sensor's resonant frequency to the soil moisture content changes over time. For experimental validation purposes, a compact sensor prototype (75 mm × 35 mm × 1.55 mm and 12 g) with fundamental resonant frequency at 958 MHz was built and submitted to calibration process on typically sandy and clay soils to get fitting models capable to estimate soil moisture content. Simulated and experimental results are presented together and show good agreement with each other. The calibration data for both soil types were processed using the least‐squares method and the proposed curve‐fitting models are capable to estimate soil moisture content levels from 5% to 20% for sandy soil and from 12% to 23% for clay soil with satisfactory accuracy, proving the device viability.

3D Printing Applications in Agriculture, Food Processing, and Environmental Protection and Monitoring

This paper presents a mini review of the applications of 3D printing, formally known as additive manufacturing, in the fields of agriculture, food processing, and the environment protection and monitoring. The paper discusses materials used in 3D printing, the different printing technologies employed in the process, as well as its prospects. PLA and ABS thermoplastics find the most application in the field of agriculture as they are affordable filaments available in the market and they are relatively easy to print. The direct extrusion of food helps people with swallowing difficulties increase their food intake as well as customize their diet. As for the environment, applications in water desalination and air quality monitoring are among the use cases of 3D printing presented in this paper.

Mapping landscape canopy nitrogen content from space using PRISMA data

Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced opportunities for the development of new-generation retrieval models of multiple vegetation traits. Among these, canopy nitrogen content (CNC) is one of the most promising variables given its importance for agricultural monitoring applications. This work presents the first hybrid CNC retrieval model for the operational delivery from spaceborne imaging spectroscopy data. To achieve this, physically-based models were combined with machine learning regression algorithms and active learning (AL). The key concepts involve: (1) coupling the radiative transfer models PROSPECT-PRO and SAIL for the generation of a wide range of vegetation states as training data, (2) using dimensionality reduction to deal with collinearity, (3) applying an AL technique in combination with Gaussian process regression (GPR) for fine-tuning the training dataset on in field collected data, and (4) adding non-vegetated spectra to enable the model to deal with spectral heterogeneity in the image. The final CNC model was successfully validated against field data achieving a low root mean square error (RMSE) of 3.4 g/m2 and coefficient of determination (R2) of 0.7. The model was applied to a PRISMA image acquired over agricultural areas in the North of Munich, Germany. Mapping aboveground CNC yielded reliable estimates over the whole landscape and meaningful associated uncertainties. These promising results demonstrate the feasibility of routinely quantifying CNC from space, such as in an operational context as part of the future CHIME mission.

Two-level K-nearest neighbors approach for invasive plants detection and classification

Invasive plants (IPs) are considered major threat to biodiversity and ecosystems due to their potential to compete for resources with other plants or crops. Timely and effective detection is important to prevent and control their growth. Recent advancements in unmanned aerial systems (UAS) have brought an effective, inexpensive, and non-invasive approach to monitoring plants. The use of UAS is promising in the accurate and rapid identification of IPs in the field. However, challenges exist when using UAS for IP detection as they generate a huge number of images that must be interpreted by researchers manually. This time-consuming and error-prone process significantly limits the applicability of UAS in a variety of environmental and agriculture monitoring applications. To reduce processing time and increase detection accuracy, we develop an automatic and intelligent detection approach to identify IPs with high levels of efficiency and accuracy from images of the monitoring areas captured by UAS. First, the original field images are segmented into patch images using the simple linear iterative clustering (SLIC) superpixels algorithm. Then a novel two-level K-nearest neighbors (TLKNN) algorithm is proposed to classify the patch images into different IP categories. In it, a deep convolutional neural network (CNN) is used to extract the features from the patch image and a kernel map function is utilized to obtain the kernel features. Two feature sets are fed to train two K-nearest neighbor (KNN) models. Finally, the final classification is determined by the selection results by KNN models. In the experiments, two public UAV image sets are employed to train and verify the proposed method. Several metrics such as accuracy, recall, precision, and F1-score, are used to evaluate the performance. The proposed TLKNN is compared with two newly proposed convolutional neural network (CNN) methods on the same datasets. Experimental results indicate the proposed TLKNN algorithm achieves better classification performance and reaches the highest accuracy, recall rate, precision rate, and F1-score. The improved performance not only means that IPs can be detected more accurately and automatically, but also that there are environmental benefits in developing an intelligent strategy for IP prevention and control.