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000911250 1001_ $$0P:(DE-Juel1)186730$$aBauer, Felix Maximilian$$b0$$eCorresponding author
000911250 245__ $$aDevelopment and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline
000911250 260__ $$aWashington, D.C.$$bAmerican Association for the Advancement of Science$$c2022
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000911250 520__ $$aRoot systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r=0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.
000911250 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000911250 536__ $$0G:(GEPRIS)390732324$$aDFG project 390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion $$c390732324$$x1
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000911250 7001_ $$0P:(DE-Juel1)180553$$aLärm, Lena$$b1
000911250 7001_ $$0P:(DE-Juel1)168106$$aMorandage, Shehan$$b2
000911250 7001_ $$0P:(DE-Juel1)171180$$aLobet, Guillaume$$b3
000911250 7001_ $$0P:(DE-Juel1)129548$$aVanderborght, Jan$$b4
000911250 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b5
000911250 7001_ $$0P:(DE-Juel1)157922$$aSchnepf, Andrea$$b6
000911250 773__ $$0PERI:(DE-600)2968615-5$$a10.34133/2022/9758532$$gVol. 2022, p. 1 - 14$$p1 - 14$$tPlant phenomics$$v2022$$x2643-6515$$y2022
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