Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Retrieval of crude protein in perennial ryegrass using spectral data at the Canopy level
    Alckmin, Gustavo Togeiro de; Lucieer, Arko ; Roerink, Gerbert ; Rawnsley, Richard ; Hoving, Idse ; Kooistra, Lammert - \ 2020
    Remote Sensing 12 (2020)18. - ISSN 2072-4292
    Crude protein - Feature selection - Hyperspectral - Machine learning - Partial least squares - Perennial ryegrass - Variable importance

    Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model's prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350-2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400-1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha-1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha-1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5-3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading.

    Experimental flight patterns evaluation for a UAV-based air pollutant sensor
    Araujo, João Otávio ; Valente, João ; Kooistra, Lammert ; Munniks, Sandra ; Peters, Ruud J.B. - \ 2020
    Micromachines 11 (2020)8. - ISSN 2072-666X
    Electrochemical sensors - Gas sensing - Remote sensing - Unmanned aerial vehicle

    The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0-2.9m/s) to high (2.1-5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. AWilcoxon rank-sum test showed significant difference between flight patterns only under HighWind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.

    Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices
    Togeiro de Alckmin, Gustavo ; Kooistra, Lammert ; Rawnsley, Richard ; Lucieer, Arko - \ 2020
    Precision Agriculture (2020). - ISSN 1385-2256
    Biomass - Canopy height - Machine learning - Perennial ryegrass - Rising plate meter - Vegetation index

    Pasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

    MOOC drones for agriculture : The making-of
    Valente, Joao ; Kooistra, Lammert - \ 2020
    In: Proceedings of the 2020 IEEE Global Engineering Education Conference, EDUCON 2020. - IEEE computer society (IEEE Global Engineering Education Conference, EDUCON ) - ISBN 9781728109312 - p. 1692 - 1695.
    Agriculture - Drones - MOOCs - Online learning - UAVs

    Imagine that there is an online course where you could learn how Drones/UAVs could be used to solve agricultural problems and contribute to the global food problem. What if you could make it yourself? We present the steps given in the design and development of the MOOC Drones for Agriculture: Prepare and Design your Drone (UAV) Mission. This MOOC is the first attempt to teach people about aerial remote sensing in agriculture. From the early stage idea to the making-of, this paper will drive you behind the scenes that made this MOOC.

    The Effect of Depth on the Morphology, Bacterial Clearance, and Respiration of the Mediterranean Sponge Chondrosia reniformis (Nardo, 1847)
    Gökalp, Mert ; Kooistra, Tjitske ; Rocha, Miguel Soares ; Silva, Tiago H. ; Osinga, Ronald ; Murk, Albertinka J. ; Wijgerde, Tim - \ 2020
    Marine Drugs 18 (2020)7. - ISSN 1660-3397
    Chondrosia reniformis - Clearance rate - Collagen - Depth - Integrated multitrophic aquaculture - Osculum size - Respiration - Sponge

    To support the successful application of sponges for water purification and collagen production, we evaluated the efiect of depth on sponge morphology, growth, physiology, and functioning. Specimens of Eastern Mediterranean populations of the sponge Chondrosia reniformis (Nardo, 1847) (Demospongiae, Chondrosiida, Chondrosiidae) were reciprocally transplanted between 5 and 20 m depth within the Kąs-Kekova Marine Reserve Area. Control sponges at 5 m had fewer but larger oscula than their conspecifics at 20 m, and a significant inverse relationship between the osculum density and size was found in C. reniformis specimens growing along a natural depth gradient. Sponges transplanted from 20 to 5 m altered their morphology to match the 5 m control sponges, producing fewer but larger oscula, whereas explants transplanted from 5 to 20 m did not show a reciprocal morphological plasticity. Despite the changes in morphology, the clearance, respiration, and growth rates were comparable among all the experimental groups. This indicates that depth-induced morphological changes do not a fiect the overall performance of the sponges. Hence, the potential for the growth and bioremediation of C. reniformis in mariculture is not likely to change with varying culture depth. The collagen content, however, was higher in shallow water C. reniformis compared to deeper-growing sponges, which requires further study to optimize collagen production.

    Automated crop plant counting from very high-resolution aerial imagery
    Valente, João ; Sari, Bilal ; Kooistra, Lammert ; Kramer, Henk ; Mücher, Sander - \ 2020
    Precision Agriculture (2020). - ISSN 1385-2256
    Crop emergence - Machine learning - Plant counting - Transfer learning - UAV RGB imagery

    Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.

    Deep learning for automated detection of Drosophila suzukii : potential for UAV-based monitoring
    Roosjen, Peter P.J. ; Kellenberger, Benjamin ; Kooistra, Lammert ; Green, David R. ; Fahrentrapp, Johannes - \ 2020
    Pest Management Science 76 (2020)9. - ISSN 1526-498X - 9 p.
    deep learning - Drosophila suzukii - integrated pest management (IPM) - object detection - unmanned aerial vehicle (UAV)

    BACKGROUND: The fruit fly Drosophila suzukii, or spotted wing drosophila (SWD), is a serious pest worldwide, attacking many soft-skinned fruits. An efficient monitoring system that identifies and counts SWD in crops and their surroundings is therefore essential for integrated pest management (IPM) strategies. Existing methods, such as catching flies in liquid bait traps and counting them manually, are costly, time-consuming and labour-intensive. To overcome these limitations, we studied insect trap monitoring using image-based object detection with deep learning. RESULTS: Based on an image database with 4753 annotated SWD flies, we trained a ResNet-18-based deep convolutional neural network to detect and count SWD, including sex prediction and discrimination. The results show that SWD can be detected with an area under the precision recall curve (AUC) of 0.506 (female) and 0.603 (male) in digital images taken from a static position. For images collected using an unmanned aerial vehicle (UAV), the algorithm detected SWD individuals with an AUC of 0.086 (female) and 0.284 (male). The lower AUC for the aerial imagery was due to lower image quality caused by stabilisation manoeuvres of the UAV during image collection. CONCLUSION: Our results indicate that it is possible to monitor SWD using deep learning and object detection. Moreover, the results demonstrate the potential of UAVs to monitor insect traps, which could be valuable in the development of autonomous insect monitoring systems and IPM.

    UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture
    Kallimani, Christina ; Heidarian Dehkordi, Ramin ; Evert, Frits van; Kooistra, Lammert ; Rijk, Bert - \ 2020
    Wageningen University & Research
    multispectral - thermal infrared - diurnal variability - unmanned aerial vehicles (UAV) - precision agriculture - wheat - potato - barley
    To explore the diurnal variations, radiometric and geometric accuracy of UAV-based data for precision agriculture, a comprehensive dataset was created in a one-day field campaign (21 June 2017). The multi-sensor data set covers wheat, barley & potato experimental fields, located in Wageningen University and Research (WUR) farm maintained by Unifarm. UAV-based images were collected with several sensors over the experimental area, starting from 7:25am and ending at 20:00pm local solar time. The dataset consists of images collected by 9 flights with senseFly MSP4C, 9 with Parrot Sequoia, 2 with Slant Range P3, 5 with DJI Zenmuse X3 NIR, 4 with the senseFly Thermo-map and 1 with the RGB Sony WX-220. Additionally, validation measurements at radiometric calibration plates and plant sample locations were taken with a Cropscan handheld spectrometer and a tec5 Handyspec spectrometer. The dataset consists of the validation measurements, the raw images and the processed orthomosaics (both with and without geometric correction).
    UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture
    Kallimani, Christina ; Heidarian, Ramin ; Evert, Frits K. van; Rijk, Bert ; Kooistra, Lammert - \ 2020
    ODjAR : open data journal for agricultural research 6 (2020). - ISSN 2352-6378 - p. 1 - 7.
    To explore the diurnal variations, radiometric and geometric accuracy of UAV-based data for precision agriculture, a comprehensive dataset was created in a one-day field campaign (21 June 2017). The multi-sensor data set covers wheat, barley & potato experimental fields, located in Wageningen University and Research (WUR) farm maintained by Unifarm. UAV-based images were collected with several sensors over the experimental area, starting from 7:25am and ending at 20:00pm local solar time. The dataset consists of images collected by 9 flights with senseFly MSP4C, 9 with Parrot Sequoia, 2 with Slant Range P3, 5 with DJI Zenmuse X3 NIR, 4 with the senseFly Thermo-map and 1 with the RGB Sony WX-220. Additionally, validation measurements at radiometric calibration plates and plant sample locations were taken with a Cropscan handheld spectrometer and a tec5 Handyspec spectrometer. The dataset consists of the validation measurements, the raw images and the processed orthomosaics (both with and without geometric correction).
    Automated Processing of Sentinel-2 Products for Time-Series Analysis in Grassland Monitoring
    Hardy, Tom ; Franceschini, Marston Domingues ; Kooistra, Lammert ; Novani, Marcello ; Richter, Sebastiaan - \ 2020
    In: International Symposium on Environmental Software Systems (ISESS 2020) Wageningen : Springer (IFIP Advances in Information and Communication Technology ) - ISBN 9783030398149 - p. 48 - 56.
    Effective grassland management practices require a good understanding of soil and vegetation properties, that can be quantified by farmers’ knowledge and remote sensing techniques. Many systems have been proposed in the past for grassland monitoring, but open-source alternatives are increasingly being preferred. In this paper, a system is proposed to process data in an open-source and automated way. This system made use of Sentinel-2 data to support grassland management at Haus Riswick in the region around Kleve, Germany, retrieved with help of a platform called Sentinelsat that was developed by ESA. Consecutive processing steps consisted of atmospheric correction, cloud masking, clipping the raster data, and calculation of vegetation indices. First results from 2018 resembled the mowing regime of the area with four growing cycles, although outliers were detected due to a lack of data caused by cloud cover. Moreover, that year’s extremely dry summer was visible in the time-series pattern as well. The proposed script is a primary version of a processing chain, which is suitable to be further expanded for more advanced data pre-processing and data analysis in the future.
    Biomass and crop height estimation of different crops using UAV-based LiDAR
    Harkel, Jelle ten; Bartholomeus, Harm ; Kooistra, Lammert - \ 2019
    Remote Sensing 12 (2019)1. - ISSN 2072-4292
    Biomass - Crop height - Field phenotyping - UAV-based LiDAR

    Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown inWageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.

    Object-Based Image Analysis Applied to Low Altitude Aerial Imagery for Potato Plant Trait Retrieval and Pathogen Detection
    Siebring, Jasper ; Valente, João ; Domingues Franceschini, Marston Heracles ; Kamp, Jan ; Kooistra, Lammert - \ 2019
    Sensors 19 (2019)24. - ISSN 1424-8220 - 15 p.
    There is a growing demand in both food quality and quantity, but as of now, one-third of all food produced for human consumption is lost due to pests and other pathogens accounting for roughly 40% of pre-harvest loss in potatoes. Pathogens in potato plants, like the Erwinia bacteria and the PVYNTN virus for example, exhibit symptoms of varying severity that are not easily captured by pixel-based classes (as these ignore shape, texture, and context in general). The aim of this research is to develop an object-based image analysis (OBIA) method for trait retrieval of individual potato plants that maximizes information output from Unmanned Aerial Vehicle (UAV) RGB very high resolution (VHR) imagery and its derivatives, to be used for disease detection of the Solanum tuberosum. The approach proposed can be split in two steps: (1) object-based mapping of potato plants using an optimized implementation of large scale mean-shift segmentation (LSMSS), and (2) classification of disease using a random forest (RF) model for a set of morphological traits computed from their associative objects. The approach was proven viable as the associative RF model detected presence of Erwinia and PVY pathogens with a maximum F1 score of 0.75 and an average Matthews Correlation Coefficient (MCC) score of 0.47. It also shows that low-altitude imagery acquired with a commercial UAV is a viable off-the-shelf tool for precision farming, and potato pathogen detection.
    Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity
    Nuijten, Rik J.G. ; Kooistra, Lammert ; Deyn, Gerlinde B. De - \ 2019
    Drones 3 (2019)3. - ISSN 2504-446X
    Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.
    Emerging technologies for biodiversity assessment of changing tropical forests
    Mulatu, Kalkidan Ayele - \ 2019
    Wageningen University. Promotor(en): M. Herold, co-promotor(en): L. Kooistra; B. Mora. - Wageningen : Wageningen University - ISBN 9789463950879 - 155

    Increasing anthropogenic pressure leads to habitat loss of tropical forests through deforestation and forest degradation. Tropical forest-dependent species are threatened with such disturbances that alter the complexity of their habitat. Measuring the structural configuration and diversity of tropical forest habitats will help explain the state of forest degradation and the resulting biodiversity dynamics. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using conventional field survey approaches. However, these approaches often fall short at addressing complex disturbance factors and responses at different spatiotemporal scales. The integration of novel monitoring approaches such as satellite remote sensing, terrestrial LIght Detection and Ranging (LiDAR), and high-throughput DNA metabarcoding have the potential to improve the detection of subtle tropical forest disturbances and responses of species to changing tropical forests, which are largely unknown. This thesis’ aim is to investigate the application of emerging satellite remote sensing and in-situ measurements to assess the complex forest biodiversity dynamics in changing tropical forests. A particular focus is given to the use of terrestrial LiDAR and satellite remote sensing for deriving forest structure parameters that inform on the state of different tropical forest habitats. For this purpose, field plots were established in the UNESCO Kafa biosphere reserve (KBR), Ethiopia. The study has identified the complementarity between remote sensing and in-situ measurements, on the bases of the primary biodiversity attributes and the essential biodiversity variables; demonstrated that the impacts of disturbance on forest structure can be captured with terrestrial LiDAR measurements; assessed the sensitivity of satellite remote sensing derived parameters to field measured structural variables; and demonstrated that the influence of forest habitat conditions on leaf-litter-arthropod composition can be identified by linking forest structure parameters that are derived from remote sensing and conventional measurement with DNA metabarcoding diversity dataset. This thesis provides a scientific contribution to the exploration of integrating technological advancements in remote sensing and in-situ measurements to derive information that is essential for assessing forest biodiversity change.

    Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR
    Brede, Benjamin ; Calders, Kim ; Lau, Alvaro ; Raumonen, Pasi ; Bartholomeus, Harm M. ; Herold, Martin ; Kooistra, Lammert - \ 2019
    Remote Sensing of Environment 233 (2019). - ISSN 0034-4257
    Above-Ground Biomass (AGB) product calibration and validation require ground reference plots at hectometric scales to match space-borne missions' resolution. Traditional forest inventory methods that use allometric equations for single tree AGB estimation suffer from biases and low accuracy, especially when dealing with large trees. Terrestrial Laser Scanning (TLS) and explicit tree modelling show high potential for direct estimates of tree volume, but at the cost of time demanding fieldwork. This study aimed to assess if novel Unmanned Aerial Vehicle Laser Scanning (UAV-LS) could overcome this limitation, while delivering comparable results. For this purpose, the performance of UAV-LS in comparison with TLS for explicit tree modelling was tested in a Dutch temperate forest. In total, 200 trees with Diameter at Breast Height (DBH) ranging from 6 to 91 cm from 5 stands, including coniferous and deciduous species, have been scanned, segmented and subsequently modelled with TreeQSM. TreeQSM is a method that builds explicit tree models from laser scanner point clouds. Direct comparison with TLS derived models showed that UAV-LS reliably modelled the volume of trunks and branches with diameter ≥30 cm in the mature beech and oak stand with Concordance Correlation Coefficient (CCC) of 0.85 and RMSE of1.12 m3. Including smaller branch volume led to a considerable overestimation and decrease in correspondence to CCC of 0.51 and increase in RMSE to 6.59 m3. Denser stands prevented sensing of trunks and further decreased CCC to 0.36 in the Norway spruce stand. Also small, young trees posed problems by preventing a proper depiction of the trunk circumference and decreased CCC to 0.01. This dependence on stand indicated a strong impact of canopy structure on the UAV-LS volume modelling capacity. Improved flight paths, repeated acquisition flights or alternative modelling strategies could improve UAV-LS modelling performance under these conditions. This study contributes to the use of UAV-LS for fast tree volume and AGB estimation on scales relevant for satellite AGB product calibration and validation.

    Fast Classification of Large Germinated Fields Via High-Resolution UAV Imagery
    Valente, João ; Kooistra, Lammert ; Mücher, Sander - \ 2019
    IEEE Robotics and Automation Letters 4 (2019)4. - ISSN 2377-3766 - p. 3216 - 3223.
    agro-food robotics - crop emergence - field assessment - machine vision - plants breeding - Unmanned aerial vehicles

    Crop breeding consists of the process of editing crop genetic profile for increasing many crop qualities. In order to achieve optimal results, crop breeders have to plant thousands of plants and keep a track of their growth almost daily. This process requires increased man-hour inspection over large fields, which results in poor accuracy due to human fatigue and a time-inefficient strategy. In this letter, two machine vision approaches were compared for classifying three crop germination classes (good, average, and bad). A naive approach using a classical segmentation and an unsupervised learning approach using k-means segmentation were compared within a high-resolution unmanned aerial vehicles imagery dataset. Experimental results demonstrate the classification of germinated patches up to 0.05 m2/patch of resolution with a minimum F1-score of 76% and 80%, and AUC of 95% and 91% for high and low spatial image resolutions, respectively.

    Automatic apple tree blossom estimation from uav rgb imagery
    Tubau Comas, A. ; Valente, J. ; Kooistra, L. - \ 2019
    In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ) - p. 631 - 635.
    Apple orchard - flowering intensity - image segmentation - Thinning - UAV

    Apple trees often produce high amount of fruits, which results in small, low quality fruits. Thinning in apple orchards is used to improve the quality of the apples by reducing the number of flowers or fruits the tree is producing. The current method used to estimate how much thinning is necessary is to measure flowering intensity, currently done by human visual inspection of trees in the orchard. The use of images of apple trees from ground-level to measure flowering intensity and its spatial variation through orchards has been researched with promising results. This research explores the potential of UAV RGB high-resolution imagery to measure flowering intensity. Image segmentation techniques have been used to segment the white pixels, which correspond to the apple flowers, of the orthophoto and the single photos. Single trees have been cropped from the single photos and from the orthophoto, and correlation has been measured between percentage of white pixels per tree and flowering intensity and between percentage of white pixels per tree and flower clusters. The resulting correlation is low, with a maximum of 0.54 for the correlation between white pixels per tree and flower clusters when using the ortophoto. Those results show the complexity of working with drone images, but there are still alternative approaches that have to investigated before discarding the use of UAV RGB imagery for estimation of flowering intensity.

    Feature filtering and selection for dry matter estimation on perennial ryegrass: A case study of vegetation indices
    Alckmin, G.T. ; Kooistra, L. ; Lucieer, A. ; Rawnsley, R. - \ 2019
    In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ) - p. 1827 - 1831.
    Biomass - Collinearity - Dry Matter - Feature Selection - Machine Learning - Pasture - Perennial Ryegrass - Vegetation Indices

    Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n Combining double low line 900), indicates that for this response variable (i.e. kg.DM.ha−1), more than 80% of indices present a high degree of collinearity (correlation > |0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error – RMSE Combining double low line 412.27 kg.DM.ha−1) and tolerable models (with a smaller number of features – 4 VIs and within 10% of the lowest RMSE.).

    Opportunities of uavs in orchard management
    Zhang, C. ; Valente, J. ; Kooistra, L. ; Guo, L. ; Wang, W. - \ 2019
    In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ) - p. 673 - 680.
    Orchard - Orchard Management - Remote Sensing - UAVs

    The growth process of fruit trees is accompanied by a large number of monitoring and management activities, such as pruning, irrigation, fertilization, spraying, and harvesting, which are labour intensive and time consuming. In the context of precision agriculture, automation and precision orchard management not only saves labour resources and increases the income of growers, but also has great significance in improving resource utilization. Recent technological developments enable Unmanned Aerial Vehicles (UAVs, also commonly referred to as Unmanned Aerial Systems, or 'drones') to become an efficient monitoring tool for improving orchard management, that can provide growers much more detailed and precise information about fruit crops health status, geometric variables, physiological variables etc. This paper reviews the use of UAVs in orchard management, with a focus on recent UAV applications, synthetically describing the existing situation (e.g., general data processing approaches, sensing platform and sensor uploaded). The challenges and prospects of UAVs opportunities in orchard management are also summarized.

    Detecting Rumex Obtusifolius weed plants in grasslands from UAV RGB imagery using deep learning
    Valente, J. ; Doldersum, M. ; Roers, C. ; Kooistra, L. - \ 2019
    In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ) - p. 179 - 185.
    Aerial surveying - Deep learning - DJI Phantom - Grasslands - Machine vision - Plant detection - Rumex - Weeding

    Broad-leaved dock (Rumex obtusifolius) is a fast growing and spreading weed and is one of the most common weeds in production grasslands in the Netherlands. The heavy occurrence, fast growth and negative environmental-agricultural impact makes Rumex a species important to control. Current control is done directly in the field by mechanical or chemical actuation methods as soon as the plants are found in situ by the farmer. In nature conservation areas control is much more difficult because spraying is not allowed. This reduces the amount of grass and its quality. Rumex could be rapidly detected using high-resolution RGB images obtained from a UAV and optimize the plant control practices in wide nature conservation areas. In this paper, a novel approach for Rumex detection from orthomosaics obtained using a commercial available quadrotor (DJI Phantom 3 PRO) is proposed. The results obtained shown that Rumex can be detected up to 90% from a 6 mm/pixel ortho-mosaic generated from an aerial survey and using deep learning.

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