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- ItemImpact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds(Basel : MDPI, 2021) Li, Minhui; Shamshiri, Redmond R.; Schirrmann, Michael; Weltzien, CorneliaEstimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at -45⸰_ and horizontally at 0⸰_ (VA -45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemDeep Learning Object Detection for Image Analysis of Cherry Fruit Fly (Rhagoletis cerasi L.) on Yellow Sticky Traps(Berlin ; Heidelberg : Springer, 2022) Salamut, Christian; Kohnert, Iris; Landwehr, Niels; Pflanz, Michael; Schirrmann, Michael; Zare, MohammadInsect populations appear with a high spatial, temporal and type-specific diversity in orchards. One of the many monitoring tools for pest management is the manual assessment of sticky traps. However, this type of assessment is laborious and time-consuming so that only a few locations can be controlled in an orchard. The aim of this study is to test state-of-the art object detection algorithms from deep learning to automatically detect cherry fruit flies (Rhagoletis cerasi), a common insect pest in cherry plantations, within images from yellow sticky traps. An image annotation database was built with images taken from yellow sticky traps with more than 1600 annotated cherry fruit flies. For better handling in the computational algorithms, the images were augmented to smaller ones by the known image preparation methods “flipping” and “cropping” before performing the deep learning. Five deep learning image recognition models were tested including Faster Region-based Convolutional Neural Network (R-CNN) with two different methods of pretraining, Single Shot Detector (SSD), RetinaNet, and You Only Look Once version 5 (YOLOv5). R‑CNN and RetinaNet models outperformed other ones with a detection average precision of 0.9. The results indicate that deep learning can act as an integral component of an automated system for high-throughput assessment of pest insects in orchards. Therefore, this can reduce the time for repetitive and laborious trap assessment but also increase the observed amount of sticky traps
- ItemImproving Deep Learning-based Plant Disease Classification with Attention Mechanism(Berlin ; Heidelberg : Springer, 2022) Alirezazadeh, Pendar; Schirrmann, Michael; Stolzenburg, FriederIn recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.
- ItemEarly Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks(Lausanne : Frontiers Media, 2021) Schirrmann, Michael; Landwehr, Niels; Giebel, Antje; Garz, Andreas; Dammer, Karl-HeinzStripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.
- ItemPrimarily tests of a optoelectronic in-canopy sensor for evaluation of vertical disease infection in cereals(New York, NY : Wiley, 2022) Dammer, Karl-Heinz; Schirrmann, MichaelBACKGROUND: Health scouting of crops by satellite, airplanes, unmanned aerial (UAV) and ground vehicles can only evaluate the crop from above. The visible leaves may show no disease symptoms, but lower, older leaves not visible from above can do. A mobile in-canopy sensor was developed, carried by a tractor to detect diseases in cereal crops. Photodiodes measure the reflected light in the red and infrared wavelength range at 10 different vertical heights in lateral directions. RESULTS: Significant differences occurred in the vegetation index NDVI of sensor levels operated inside and near the winter wheat canopy between infected (stripe rust: 2018, 2019 / leaf rust: 2020) and control plots. The differences were not significant at those sensor levels operated far above the canopy. CONCLUSIONS: Lateral reflectance measurements inside the crop canopy are able to distinguish between disease-infected and healthy crops. In future mobile in-canopy scouting could be an extension to the common above-canopy scouting praxis for making spraying decisions by the farmer or decision support systems. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
- ItemRapid and low-cost insect detection for analysing species trapped on yellow sticky traps(London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, MichaelWhile insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role in increased acceptance and application of IPM in practice. In this study, we tested the differentiation of two whitefly species and their natural enemies trapped on yellow sticky traps (YSTs) via image processing approaches under practical conditions. Using the bag of visual words (BoVW) algorithm, accurate differentiation between both natural enemies and the Trialeurodes vaporariorum and Bemisia tabaci species was possible, whereas the procedure for B. tabaci could not be used to differentiate this species from T. vaporariorum. The decay of species was considered using fresh and aged catches of all the species on the YSTs, and different pooling scenarios were applied to enhance model performance. The best performance was reached when fresh and aged individuals were used together and the whitefly species were pooled into one category for model training. With an independent dataset consisting of photos from the YSTs that were placed in greenhouses and consequently with a naturally occurring species mixture as the background, a differentiation rate of more than 85% was reached for natural enemies and whiteflies.
- ItemGlobal data on earthworm abundance, biomass, diversity and corresponding environmental properties(London : Nature Publ. Group, 2021) Phillips, Helen R. P.; Bach, Elizabeth M.; Bartz, Marie L. C.; Bennett, Joanne M.; Beugnon, Rémy; Briones, Maria J. I.; Brown, George G.; Ferlian, Olga; Gongalsky, Konstantin B.; Guerra, Carlos A.; König-Ries, Birgitta; López-Hernández, Danilo; Loss, Scott R.; Marichal, Raphael; Matula, Radim; Minamiya, Yukio; Moos, Jan Hendrik; Moreno, Gerardo; Morón-Ríos, Alejandro; Motohiro, Hasegawa; Muys, Bart; Krebs, Julia J.; Neirynck, Johan; Norgrove, Lindsey; Novo, Marta; Nuutinen, Visa; Nuzzo, Victoria; Mujeeb Rahman, P.; Pansu, Johan; Paudel, Shishir; Pérès, Guénola; Pérez-Camacho, Lorenzo; Orgiazzi, Alberto; Ponge, Jean-François; Prietzel, Jörg; Rapoport, Irina B.; Rashid, Muhammad Imtiaz; Rebollo, Salvador; Rodríguez, Miguel Á.; Roth, Alexander M.; Rousseau, Guillaume X.; Rozen, Anna; Sayad, Ehsan; Ramirez, Kelly S.; van Schaik, Loes; Scharenbroch, Bryant; Schirrmann, Michael; Schmidt, Olaf; Schröder, Boris; Seeber, Julia; Shashkov, Maxim P.; Singh, Jaswinder; Smith, Sandy M.; Steinwandter, Michael; Russell, David J.; Szlavecz, Katalin; Talavera, José Antonio; Trigo, Dolores; Tsukamoto, Jiro; Uribe-López, Sheila; de Valença, Anne W.; Virto, Iñigo; Wackett, Adrian A.; Warren, Matthew W.; Webster, Emily R.; Schwarz, Benjamin; Wehr, Nathaniel H.; Whalen, Joann K.; Wironen, Michael B.; Wolters, Volkmar; Wu, Pengfei; Zenkova, Irina V.; Zhang, Weixin; Cameron, Erin K.; Eisenhauer, Nico; Wall, Diana H.; Brose, Ulrich; Decaëns, Thibaud; Lavelle, Patrick; Loreau, Michel; Mathieu, Jérôme; Mulder, Christian; van der Putten, Wim H.; Rillig, Matthias C.; Thakur, Madhav P.; de Vries, Franciska T.; Wardle, David A.; Ammer, Christian; Ammer, Sabine; Arai, Miwa; Ayuke, Fredrick O.; Baker, Geoff H.; Baretta, Dilmar; Barkusky, Dietmar; Beauséjour, Robin; Bedano, Jose C.; Birkhofer, Klaus; Blanchart, Eric; Blossey, Bernd; Bolger, Thomas; Bradley, Robert L.; Brossard, Michel; Burtis, James C.; Capowiez, Yvan; Cavagnaro, Timothy R.; Choi, Amy; Clause, Julia; Cluzeau, Daniel; Coors, Anja; Crotty, Felicity V.; Crumsey, Jasmine M.; Dávalos, Andrea; Cosín, Darío J. Díaz; Dobson, Annise M.; Domínguez, Anahí; Duhour, Andrés Esteban; van Eekeren, Nick; Emmerling, Christoph; Falco, Liliana B.; Fernández, Rosa; Fonte, Steven J.; Fragoso, Carlos; Franco, André L. C.; Fusilero, Abegail; Geraskina, Anna P.; Gholami, Shaieste; González, Grizelle; Gundale, Michael J.; López, Mónica Gutiérrez; Hackenberger, Branimir K.; Hackenberger, Davorka K.; Hernández, Luis M.; Hirth, Jeff R.; Hishi, Takuo; Holdsworth, Andrew R.; Holmstrup, Martin; Hopfensperger, Kristine N.; Lwanga, Esperanza Huerta; Huhta, Veikko; Hurisso, Tunsisa T.; Iannone, Basil V.; Iordache, Madalina; Irmler, Ulrich; Ivask, Mari; Jesús, Juan B.; Johnson-Maynard, Jodi L.; Joschko, Monika; Kaneko, Nobuhiro; Kanianska, Radoslava; Keith, Aidan M.; Kernecker, Maria L.; Koné, Armand W.; Kooch, Yahya; Kukkonen, Sanna T.; Lalthanzara, H.; Lammel, Daniel R.; Lebedev, Iurii M.; Le Cadre, Edith; Lincoln, Noa K.Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.
- ItemPublisher Correction: Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps(London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, MichaelCorrection to: Scientific Reports https://doi.org/10.1038/s41598-021-89930-w, published online 17 May 2021
- ItemOptimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops(Basel : MDPI AG, 2021) de Camargo, Tibor; Schirrmann, Michael; Landwehr, Niels; Dammer, Karl-Heinz; Pflanz, MichaelWeed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h−1 area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
- ItemUAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds(Basel : MDPI, 2022) Li, Minhui; Shamshiri, Redmond R.; Schirrmann, Michael; Weltzien, Cornelia; Shafian, Sanaz; Laursen, Morten StigaardLeaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90 . The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions.