Automated airborne pest monitoring of drosophila suzukii in crops and natural habitats

Drosophila suzukii has become a serious pest in Europe attacking many soft-skinned crops such as several berry species and grapevines since its spread in 2008 to Spain and Italy. An efficient and accurate monitoring system to identify the presence of Drosophila suzukii in crops and their surroundings is essential for the prevention of damage to economically valuable fruit crops. Existing methods for monitoring Drosophila suzukii are costly, time and labor intensive, prone to errors, and typically conducted at a low spatial resolution. To overcome current monitoring limitations, we are developing a novel system consisting of sticky traps which are monitored by means of Unmanned Aerial Vehicles (UAVs) and an image processing pipeline that automatically identifies and counts the number of Drosophila suzukii per trap location. To this end, we are currently collecting high resolution RGB imagery of Drosophila suzukii flies in sticky traps taken from both a static position (tripod) and from a UAV, which are then used as input to train deep learning models. Preliminary results show that a large part of the of Drosophila suzukii flies that are caught in the sticky traps can be correctly identified by the trained deep learning models. In the future, an autonomously flying UAV platform will be programmed to capture imagery of the sticky traps under field conditions. The collected imagery will be transferred directly to cloud-based storage for subsequent processing and analysis to identify the presence and count of Drosophila suzukii in near real time. This data will be used as input to a decision support system (DSS) to provide valuable information for farmers

Measuring and modeling the effect of surface moisture on the spectral reflectance of coastal beach sand

Surface moisture is an important supply limiting factor for aeolian sand transport, which is the primary driver of coastal dune development. As such, it is critical to account for the control of surface moisture on available sand for dune building. Optical remote sensing has the potential to measure surface moisture at a high spatio-temporal resolution. It is based on the principle that wet sand appears darker than dry sand: it is less reflective. The goals of this study are (1) to measure and model reflectance under controlled laboratory conditions as function of wavelength () and surface moisture () over the optical domain of 350–2500 nm, and (2) to explore the implications of our laboratory findings for accurately mapping the distribution of surface moisture under natural conditions. A laboratory spectroscopy experiment was conducted to measure spectral reflectance (1 nm interval) under different surface moisture conditions using beach sand. A non-linear increase of reflectance upon drying was observed over the full range of wavelengths. Two models were developed and tested. The first model is grounded in optics and describes the proportional contribution of scattering and absorption of light by pore water in an unsaturated sand matrix. The second model is grounded in soil physics and links the hydraulic behaviour of pore water in an unsaturated sand matrix to its optical properties. The optical model performed well for volumetric moisture content 24% ( 0.97), but underestimated reflectance for between 24–30% ( 0.92), most notable around the 1940 nm water absorption peak. The soil-physical model performed very well ( 0.99) but is limited to 4% 24%. Results from a field experiment show that a short-wave infrared terrestrial laser scanner ( = 1550 nm) can accurately relate surface moisture to reflectance (standard error 2.6%), demonstrating its potential to derive spatially extensive surface moisture maps of a natural coastal beach

A Laboratory Goniometer System for Measuring Reflectance and Emittance Anisotropy

In this paper, a laboratory goniometer system for performing multi-angular measurements under controlled illumination conditions is described. A commercially available robotic arm enables the acquisition of a large number of measurements over the full hemisphere within a short time span making it much faster than other goniometers. In addition, the presented set-up enables assessment of anisotropic reflectance and emittance behaviour of soils, leaves and small canopies. Mounting a spectrometer enables acquisition of either hemispherical measurements or measurements in the horizontal plane. Mounting a thermal camera allows directional observations of the thermal emittance. This paper also presents three showcases of these different measurement set-ups in order to illustrate its possibilities. Finally, suggestions for applying this instrument and for future research directions are given, including linking the measured reflectance anisotropy with physically-based anisotropy models on the one hand and combining them with field goniometry measurements for joint analysis with remote sensing data on the other hand. The speed and flexibility of the system offer a large added value to the existing pool of laboratory goniometers

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30° view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450–915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements

UAV-based multi-angular measurements for improved crop parameter retrieval

Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.</p

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30° view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450–915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurement

Deep learning for automated detection of Drosophila suzukii : potential for UAV-based monitoring

Funding Information: This work is part of the research programme ERA‐net C‐IPM 2016 with project number ALW.FACCE.7, which is (partly) financed by the Dutch Research Council (NWO). In Switzerland the project was funded by the Swiss Federal Office of Agriculture (grant 627000782). In the UK the project was supported by DEFRA. Funding Information: This work is part of the research programme ERA-net C-IPM 2016 with project number ALW.FACCE.7, which is (partly) financed by the Dutch Research Council (NWO). In Switzerland the project was funded by the Swiss Federal Office of Agriculture (grant 627000782). In the UK the project was supported by DEFRA. Publisher Copyright: © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.Peer reviewedPublisher PD

AAPM : automated airborne pest monitoring

Spotted wing drosophila (SWD) is a fruit fly attacking soft berry fruits. To measure the success of control measures against SWD, accurate and frequent monitoring of its presence is required. The AAPM project develops a system based on planar, photographable such as sticky traps that are monitored with high-resolution airborne imagery. The images are analyzed with deep learning methods and target insects are counted. Currently, we selected the best performing trap to catch SWD. Some hundred traps with SWD were used to train detection and counting algorithms. The next part of the project will focus on airborne image acquisition and integration of SWD counts into decision support systems providing information for IPM strategies