TY  - JOUR
A1  - Schirrmann, Michael
A1  - Landwehr, Niels
A1  - Giebel, Antje
A1  - Garz, Andreas
A1  - Dammer, Karl-Heinz
T1  - Early detection of stripe rust in winter wheat using deep residual neural networks
JF  - Frontiers in plant science : FPLS
N2  - Stripe 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 x 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.
KW  - yellow rust
KW  - monitoring
KW  - deep learning
KW  - wheat crops
KW  - image recognition
KW  - camera sensor
KW  - ResNet
KW  - smart farming
Y1  - 2021
U6  - https://doi.org/10.3389/fpls.2021.469689
SN  - 1664-462X
VL  - 12
PB  - Frontiers Media
CY  - Lausanne
ER  - 
TY  - GEN
A1  - Rud, R.
A1  - Käthner, Jana
A1  - Giesser, J.
A1  - Pasche, R.
A1  - Giebel, Antje
A1  - Selbeck, Jörn
A1  - Shenderey, C.
A1  - Fleury, D.
A1  - Zude, Manuela
A1  - Alchanatis, Victor
T1  - Monitoring spatial variability in an apple orchard under different water regimes
T2  - International Symposium on Sensing Plant Water Status - Methods and Applications in Horticultural Science
N2  - Precision fruticulture addresses site or tree-adapted crop management. In the present study, soil and tree status, as well as fruit quality at harvest were analysed in a commercial apple (Malus × domestica 'Gala Brookfield'/Pajam1) orchard in a temperate climate. Trees were irrigated in addition to precipitation. Three irrigation levels (0, 50 and 100%) were applied. Measurements included readings of apparent electrical conductivity of soil (ECa), stem water potential, canopy temperature obtained by infrared camera, and canopy volume estimated by LiDAR and RGB colour imaging. Laboratory analyses of 6 trees per treatment were done on fruit considering the pigment contents and quality parameters. Midday stem water potential (SWP), normalized crop water stress index (CWSI) calculated from thermal data, and fruit yield and quality at harvest were analysed. Spatial patterns of the variability of tree water status were estimated by CWSI imaging supported by SWP readings. CWSI ranged from 0.1 to 0.7 indicating high variability due to irrigation and precipitation. Canopy volume data were less variable. Soil ECa appeared homogeneous in the range of 0 to 4 mS m-1. Fruit harvested in a drought stress zone showed enhanced portion of pheophytin in the chlorophyll pool. Irrigation affected soluble solids content and, hence, the quality of fruit. Overall, results highlighted that spatial variation in orchards can be found even if marginal variability of soil properties can be assumed.
KW  - apple
KW  - CWSI
KW  - precision agriculture
KW  - management zone
Y1  - 2018
SN  - 978-94-62611-93-1
U6  - https://doi.org/10.17660/ActaHortic.2018.1197.19
SN  - 0567-7572
SN  - 2406-6168
VL  - 1197
SP  - 139
EP  - 146
PB  - International Society for Horticultural Science
CY  - The Hague
ER  -