Drones provide spatial and volumetric data to deliver new insights into microclimate modelling

This is the final version. Available on open access from Springer via the DOI in this recordContext Microclimate (fine-scale temperature variability within metres of Earth’s surface) is highly influential on terrestrial organisms’ ability to survive and function. Understanding how such local climatic conditions vary is challenging to measure at adequate spatio-temporal resolution. Microclimate models provide the means to address this limitation, but require as inputs, measurements, or estimations of multiple environmental variables that describe vegetation and terrain variation. Objectives To describe the key components of microclimate models and their associated environmental parameters. To explore the potential of drones to provide scale relevant data to measure such environmental parameters. Methods We explain how drone-mounted sensors can provide relevant data in the context of alternative remote sensing products. We provide examples of how direct micro-meteorological measurements can be made with drones. We show how drone-derived data can be incorporated into 3-dimensional radiative transfer models, by providing a realistic representation of the landscape with which to model the interaction of solar energy with vegetation. Results We found that for some environmental parameters (i.e. topography and canopy height), data capture and processing techniques are already established, enabling the production of suitable data for microclimate models. For other parameters such as leaf size, techniques are still novel but show promise. For most parameters, combining spatial landscape characterization from drone data and ancillary data from lab and field studies will be a productive way to create inputs at relevant spatio-temporal scales. Conclusions Drones provide an exciting opportunity to quantify landscape structure and heterogeneity at fine resolution which are in turn scale-appropriate to deliver new microclimate insights.Met Office Hadley Centre Climate ProgrammeEuropean Regional Development Fund (ERDF)European Union Horizon 202

Identifying Advantages and Disadvantages of Variable Rate Irrigation – An Updated Review

Variable rate irrigation (VRI) sprinklers on mechanical move irrigation systems (center pivot or lateral move) have been commercially available since 2004. Although the number of VRI, zone or individual sprinkler, systems adopted to date is lower than expected there is a continued interest to harness this technology, especially when climate variability, regulatory nutrient management, water conservation policies, and declining water for agriculture compound the challenges involved for irrigated crop production. This article reviews the potential advantages and potential disadvantages of VRI technology for moving sprinklers, provides updated examples on such aspects, suggests a protocol for designing and implementing VRI technology and reports on the recent advancements. The advantages of VRI technology are demonstrated in the areas of agronomic improvement, greater economic returns, environmental protection and risk management, while the main drawbacks to VRI technology include the complexity to successfully implement the technology and the lack of evidence that it assures better performance in net profit or water savings. Although advances have been made in VRI technologies, its penetration into the market will continue to depend on tangible and perceived benefits by producers

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

Comparison of stationary and mobile canopy sensing systems for irrigation management of maize and soybean in Nebraska

Accurate knowledge of plant and field characteristics is crucial for irrigation management. Irrigation can potentially be better managed by utilizing data collected from various sensors installed on different platforms. The accuracy and repeatability of each data source are important considerations when selecting a sensing system suitable for irrigation management. The objective of this study was to compare data from multispectral (red and near-infrared bands) and thermal (long wave thermal infrared band) sensors mounted on different platforms to investigate their comparative usability and accuracy. The different sensor platforms included stationary posts fixed on the ground, the lateral of a center pivot irrigation system, unmanned aircraft systems (UAS), and Planet (PlanetScope multispectral imager, Planet Labs, Inc., San Francisco, Calif.) satellites. The surface reflectance data from multispectral (MS) sensors were used to compute the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). The experimental plots were managed with rainfed and irrigated treatments. Irrigation was applied according to a spatial evapotranspiration model informed with Planet satellite imagery. The NDVI and SAVI curves computed from the different sensing systems exhibited similar patterns and were able to capture differences between the rainfed and irrigated treatments when the crops were approaching senescence. Strong correlations were observed for canopy temperature measurements between the stationary and pivot-mounted infrared thermometer (IRT) sensors (p-value of less than 0.01 for the correlations) when canopy were scanned with no irrigation application (dry scans). The best correlation was obtained for the irrigated maize, which yielded r2 of 0.99, RMSE of 0.4°C, and MAE of 0.3°C. The correlation for the canopy temperature data collected during dry scan between UAS and pivot-mounted thermal sensors was weak with r2 = 0.26 to 0.28, larger RMSE values of 3.7°C and MAE values of 3.4°C. Secondary analysis between thermal data from stationary and pivot-mounted IRTs collected during wet scans (during an irrigation event) demonstrated reduced canopy temperature from pivot-mounted IRTs by approximately 2°C for irrigated soybean due to wetting of the canopy by the irrigation. Understanding the performance of these sensor systems is valuable in configuring practical design and operational considerations when using sensor feedback for irrigation management

Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry

Filling the sensor gap: applying UAS technology to land-use research

Collecting data at ground level typically yields the most detailed information on a subject, however it is limited by the spatial extent that can be covered within a specific timeframe. Remote sensing from an aerial platform increases this spatial extent and the deployment of unmanned aircraft systems (UAS) can provide this ability directly to researchers at an affordable cost and at data resolutions that are very applicable for site specific surveys. Further to this, developments in photogrammetry software allow the creation of orthorectified spectral and structural data that can that can be classified via pixel or object-based analysis methods and applied to a wide variety of different land-use research areas. In this study a sensor package was created consisting of two off the shelf digital cameras, one un-modified and the other modified to be sensitive to near infra-red wavelengths of light. A multi-rotor aerial platform utilising an open source autopilot was assembled to enable data collection and a processing pipeline was devised to transform RAW camera imagery into georeferenced and orthorectified data, using computer vision software following the structure from motion (SfM) approach. This remote sensing tool was applied to a variety of land-use research study sites in central Scotland and Northern England with two main areas focused on. (1) The use of spectral and structural data for the detection of disease within a potato (Solanum tuberosum L.) crop revealed that UAS could be an effective tool for mapping the distribution of diseased plants. (2) Comparisons between aerial data and traditional manual assessments of a trial crop of potatoes revealed that the earliest stages of plant emergence could not be detected but later plant counts, and ground cover estimates correlated well, indicating that UAS could be an effective trials monitoring tool, giving extra structural data and potentially a more representative measure of canopy ground cover compared to traditional manual techniques. This study also showed results from experimental applications investigating the mapping of invasive non-native species and ways of enabling upscaling of greenhouse gas emissions from different land use types. Therefore, this study demonstrates that UAS equipped with basic imaging technology can be of use to a variety of land-use research areas and look set to become an invaluable remote sensing tool, which will improve further with the addition of calibrated multi-spectral sensor payloads, high precision global navigation satellite systems and relaxation in regulations governing their use

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Vegetation Index and Dynamics

The book contemplates different ways of approaching the study of vegetation as well as the type of indices to be used. However, all the works pursue the same objective: to know and interpret nature from different points of view, either through knowledge of nature in situ or the use of technology and mapping using satellite images. Chapters analyze the ecological parameters that affect vegetation, the species that make up plant communities, and the influence of humans on vegetation