Sorghum wird in Bayern als Energiepflanze vor allem für die Biogasproduktion angebaut. Die hohe Biomasseleistung und die große Sortenvarietät in Verbindung mit seiner Trockenheitstoleranz und Nährstoffeffizienz machen Sorghum zu einer vielversprechenden Rohstoffpflanze. Neuartige Technologien, verknüpft mit intelligenter Software, eröffnen große Potentiale im Bereich der Effizienzsteigerung in der Landwirtschaft. Mit Hilfe von modernsten Verfahren des maschinellen Lernens, wie künstliche neuronale Netze oder Deep Learning, können drohnenbasierte Bildaufnahmen der Anbauflächen analysiert und Beikraut erkannt werden.
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Raymond Ajekwe
,
Michael Grieb
,
Nikita Genze
,
Dominik Grimm
Neuartige Technologien, verknüpft mit intelligenter Bildauswertung, eröffnen große Poten- ziale im Bereich der Effizienzsteigerung in der Landwirtschaft. Mit Hilfe von modernsten Ver- fahren des maschinellen Lernens (z. B. künstliche neuronale Netze) sollen drohnenbasierte Bildaufnahmen von Sorghum-Anbauflächen automatisch analysiert und Beikraut erkannt werden. Sorghum wird in Bayern als Energiepflanze vor allem für die Biogasproduktion an- gebaut. Die hohe Biomasseleistung und die große Sortenvarietät in Verbindung mit seiner Trockenheitstoleranz und Nährstoffeffizienz machen Sorghum zu einer vielversprechenden Rohstoffpflanze.
Beikräuter sind Pflanzen, die auf landwirtschaftlichen Flächen wachsen und durch Ressourcenkonkurrenz den Ertrag und die Qualität der Ernte verringern. Zur Bekämpfung der Beikräuter stehen verschiedene Strategien und Maßnahmen zur Verfügung. Am häufigsten werden dabei Herbizide eingesetzt, obwohl Risiken für die menschliche Gesundheit und die Umwelt bekannt sind. Zur Einsparung der Pflanzenschutzmittel wird daher ein standortspezifisches Beikrautmanagement empfohlen. Dafür ist eine präzise Erkennung und Klassifizierung von Beikräutern erforderlich. Unbemannte Luftfahrzeuge (Unmanned Aereal Vehicle = UAV) sind nützliche Werkzeuge zur Erfassung hochauflösender Felddaten für die Bildsegmentierung mit Deep-Learning-Methoden. Im Gegensatz zu anderen Bildaufnahmesystemen wie konventionellen Kameras, Satelliten und Flugzeugen sind UAVs kostengünstig, schnell und erfassen aufgrund geringerer Flughöhe hochwertige Bilder ohne eine Störung durch Wolken.In diesem Projekt wurde die Eignung von UAVs für die Erzeugung von Felddaten für die Früherkennung von Beikraut in Sorghum untersucht. Unterschiedliche Aufnahmebedingungen und UAV-Einstellungen wurden evaluiert, um die Bildqualität zu optimieren. Diese Bilder wurden anschließend manuell annotiert, was zu einem großen, vielfältigen und hochwertigen UAV-Beikrautdatensatz für Sorghum führte. Mithilfe dieses Datensatzes wurden mehrere moderne Deep-Learning-Modelle trainiert und auf ihre Generalisierungsfähigkeit unter Berücksichtigung von Faktoren wie Bewegungsunschärfe, direkter Sonneneinstrahlung und unterschiedlichen Wachstumsstadien, getestet.Die entwickelten Modelle waren in der Lage, dikotyle Beikräuter in einer Vielzahl von Drohnenbildern und Wachstumsstadien von Sorghum mit hoher Genauigkeit zu erkennen, hatten jedoch Schwierigkeiten mit monokotylen Beigräsern. Um Bewegungsunschärfe zu vermindern, welche als eine der Hauptfaktoren für Qualitätsverluste gilt, wurde ein zweistufiges Deep-Learning Verfahren entwickelt. Dieses verbessert vor der Segmentierung die Bildqualität. Darüber hinaus erfolgte die Entwicklung eines generativen Modells, um künstliche Bilder von Beikräutern und die dazugehörigen Segmentierungsmasken zu erzeugen. Diese generierten Bildpaare können dann verwendet werden, um neue KI-Modelle für die Beikrautsegmentierung zu trainieren, wenn die Trainingsdaten spärlich sind.Schließlich wurde ein Anpassungstest für Mais durchgeführt, um die Übertragbarkeit des Modells auf andere Kulturarten zu überprüfen. Es zeigte gute Ergebnisse in frühen Wachstumsstadien (BBCH 13 und 15), jedoch war die Unterscheidung zwischen Mais und Beikräutern in späteren Wachstumsstadien eingeschränkt. Dies weist auf den weiteren Forschungsbedarf hin.
Sustainable weed management strategies are critical to feeding the world’s population while preserving ecosystems and biodiversity. Therefore, site-specific weed control strategies based on automation are needed to reduce the additional time and effort required for weeding. Machine vision-based methods appear to be a promising approach for weed detection, but require high quality data on the species in a specific agricultural area. Here we present a dataset, the Moving Fields Weed Dataset (MFWD), which captures the growth of 28 weed species commonly found in sorghum and maize fields in Germany. A total of 94,321 images were acquired in a fully automated, high-throughput phenotyping facility to track over 5,000 individual plants at high spatial and temporal resolution. A rich set of manually curated ground truth information is also provided, which can be used not only for plant species classification, object detection and instance segmentation tasks, but also for multiple object tracking.
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Nikita Genze
,
Maximilian Wirth
,
Christian Schreiner
,
Raymond Ajekwe
,
Michael Grieb
,
Dominik Grimm
BackgroundEfficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks.ResultsIn this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmentation in motion blurred images. For this purpose, we first collected a new dataset of matching sharp and naturally blurred image pairs of real sorghum and weed plants from drone images of the same agricultural field. The data was used to train and evaluate the performance of DeBlurWeedSeg on both sharp and blurred images of a hold-out test-set. We show that DeBlurWeedSeg outperforms a standard segmentation model that does not include an integrated deblurring step, with a relative improvement of 13.4% in terms of the Sørensen-Dice coefficient.ConclusionOur combined deblurring and segmentation model DeBlurWeedSeg is able to accurately segment weeds from sorghum and background, in both sharp as well as motion blurred drone captures. This has high practical implications, as lower error rates in weed and crop segmentation could lead to better weed control, e.g. when using robots for mechanical weed removal.
Genomic studies often attempt to link natural genetic variation with important phenotypic variation. To succeed, robust and reliable phenotypic data, as well as curated genomic assemblies, are required. Wild sunflowers, originally from North America, are adapted to diverse and often extreme environments and have historically been a widely used model plant system for the study of population genomics, adaptation, and speciation. Moreover, cultivated sunflower, domesticated from a wild relative (Helianthus annuus) is a global oil crop, ranking fourth in production of vegetable oils worldwide. Public availability of data resources both for the plant research community and for the associated agricultural sector, are extremely valuable. We have created HeliantHOME (http://www.helianthome.org), a curated, public, and interactive database of phenotypes including developmental, structural and environmental ones, obtained from a large collection of both wild and cultivated sunflower individuals. Additionally, the database is enriched with external genomic data and results of genome-wide association studies. Finally, being a community open-source platform, HeliantHOME is expected to expand as new knowledge and resources become available.
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Nikita Genze
,
Raymond Ajekwe
,
Zeynep Güreli
,
Florian Haselbeck
,
Michael Grieb
,
Dominik Grimm
Weeds are undesired plants in agricultural fields that affect crop yield and quality by competing for nutrients, water, sunlight and space. For centuries, farmers have used several strategies and resources to remove weeds. The use of herbicide is still the most common control strategy. To reduce the amount of herbicide and impact caused by uniform spraying, site-specific weed management (SSWM) through variable rate herbicide application and mechanical weed control have long been recommended. To implement such precise strategies, accurate detection and classification of weeds in crop fields is a crucial first step. Due to the phenotypic similarity between some weeds and crops as well as changing weather conditions, it is challenging to design an automated system for general weed detection. For efficiency, unmanned aerial vehicles (UAV) are commonly used for image capturing. However, high wind pressure and different drone settings have a severe effect on the capturing quality, what potentially results in degraded images, e.g., due to motion blur. In this paper, we investigate the generalization capabilities of Deep Learning methods for early weed detection in sorghum fields under such challenging capturing conditions. For this purpose, we developed weed segmentation models using three different state-of-the-art Deep Learning architectures in combination with residual neural networks as feature extractors.We further publish a manually annotated and expert-curated UAV imagery dataset for weed detection in sorghum fields under challenging conditions. Our results show that our trained models generalize well regarding the detection of weeds, even for degraded captures due to motion blur. An UNet-like architecture with a ResNet-34 feature extractor achieved an F1-score of over 89 % on a hold-out test-set. Further analysis indicate that the trained model performed well in predicting the general plant shape, while most misclassifications appeared at borders of the plants. Beyond that, our approach can detect intra-row weeds without additional information as well as partly occluded plants in contrast to existing research.All data, including the newly generated and annotated UAV imagery dataset, and code is publicly available on GitHub: https://github.com/grimmlab/UAVWeedSegmentation and Mendeley Data: https://doi.org/10.17632/4hh45vkp38.3
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Nikita Genze
,
Richa Bharti
,
Michael Grieb
,
Sebastian J. Schultheiss
,
Dominik Grimm
BackgroundAssessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.ResultsWe generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cerealeand Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings.ConclusionOur proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
The aim is to develop and validate maps of the spatial distribution pattern of weed infestation using drone photos in sorghum and maize - as a basis for site-specific mechanical or …
Neuartige Technologien, verknüpft mit intelligenter Software, eröffnen große Potenziale im Bereich der Effizienzsteigerung in der Landwirtschaft. Durch den integrierten Pflanzenschutz, der aus …
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