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Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear,Multirater Dataset

Plant Phenomics 2023年第3期5卷 533-545页

作者： DominikRößle LukasPrey LudwigRamgraber AnjaHanemann DanielCremers Patrick OleNoack TorstenSchön AImotion Bavaria Technische Hochschule IngolstadtIngolstadtGermany hochschule weihenstephan-triesdorf WeidenbachGermany Saatzucht Josef Breun GmbH and Co.KG HerzogenaurachGermany Technical University of Munich MunichGermany

Fusarium head blight(FHB)is one of the most prevalent wheat diseases,causing substantial yield losses and health *** phenotyping of FHB is crucial for accelerating resistance breeding,but currently used methods are time-consuming and *** present article suggests a noninvasive classification model for FHB severity estimation using red–green–blue(RGB)images,without requiring extensive *** model accepts images taken from consumer-grade,low-cost RGB cameras and classifies the FHB severity into 6 ordinal *** addition,we introduce a novel dataset consisting of around 3,000 images from 3 different years(2020,2021,and 2022)and 2 FHB severity assessments per image from independent *** used a pretrained EfficientNet(size b0),redesigned as a regression *** results demonstrate that the interrater reliability(Cohen’s kappa,κ)is substantially lower than the achieved individual network-to-rater results,e.g.,0.68 and 0.76 for the data captured in 2020,*** model shows a generalization effect when trained with data from multiple years and tested on data from an independent ***,using the images from 2020 and 2021 for training and 2022 for testing,we improved the F_(1)^(w) score by 0.14,the accuracy by 0.11,κ by 0.12,and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year’s *** proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB *** source code and the dataset are available at https://***/cvims/FHB_classification.

关键词： network consuming breeding

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Animal Behavior in a Fully Automatically Controlled Dairy Farm

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Journal of Agricultural Science and Technology(A) 2015年第1期5卷 61-67页

作者： Anja Graiff Renate Luise Dorfler Manfred Hold Jorn Stumpenhausen Heinz Bernhardt Technische Universitat Miinchen Center of Life and Food Sciences Agricultural Systems Engineering Am Staudengarten 2D-85354 Freising- Weihenstephan Germany hochschule weihenstephan-triesdorf University of Applied Sciences weihenstephan-triesdorf Faculty of Agricultural andNutritional Sciences Am Hofgarten 1 D-85354 Freising-Weihenstephan Germany

As there are increasing numbers of small farms in Germany, it is necessary for them, to keep the workload as small as possible by enhancing the use of automatism. Important as those energetic-technical capabilities and features in a modem dairy farm are, the final decision when using automatic machinery depends on the animal itself and its behavior. As a result, all animal-physiological criteria, animal protection and animal welfare have to be taken into consideration. Therefore tests have been done to investigate dairy cattle behavior on suddenly occurring energy failures, fluctuations or postponements due to a load management. The experiments have been taken in four different stables. In each stable, 12 ＂focus cows＂ have been selected. Their daily stress response was measured by a heart rate monitor and faecal cortisol metabolites. Video observation showed modifications in behavior, escape or avoidance reactions; pedometers recorded the movement activity. These scientific experiments will demonstrate cattle behavior in situations driven by a power load management, but no statistically significant effects on the usual daily routine are being expected.

关键词： Cattle behavior cortisol heart rate variability power load management smart grid Germany.

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