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Open AccessArticle

Approach for Image-Based Semantic Segmentation of Canopy Cover in Pea–Oat Intercropping

by Sebastian Munz 1,* and David Reiser 2,*
1
Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany
2
Institute of Agricultural Engineering, University of Hohenheim, 70599 Stuttgart, Germany
*
Authors to whom correspondence should be addressed.
Agriculture 2020, 10(8), 354; https://doi.org/10.3390/agriculture10080354
Received: 2 July 2020 / Revised: 9 August 2020 / Accepted: 11 August 2020 / Published: 13 August 2020
(This article belongs to the Special Issue Agricultural Diversification)
Intercropping systems of cereals and legumes have the potential to produce high yields in a more sustainable way compared to sole cropping systems. Their agronomic optimization remains a challenging task given the numerous management options and the complexity of interactions between the crops. Efficient methods for analyzing the influence of different management options are needed. The canopy cover of each crop in the intercropping system is a good determinant for light competition, thus influencing crop growth and weed suppression. Therefore, this study evaluated the feasibility to estimate canopy cover within an intercropping system of pea and oat based on semantic segmentation using a convolutional neural network. The network was trained with images from three datasets during early growth stages comprising canopy covers between 4% and 52%. Only images of sole crops were used for training and then applied to images of the intercropping system. The results showed that the networks trained on a single growth stage performed best for their corresponding dataset. Combining the data from all three growth stages increased the robustness of the overall detection, but decreased the accuracy of some of the single dataset result. The accuracy of the estimated canopy cover of intercropped species was similar to sole crops and satisfying to analyze light competition. Further research is needed to address different growth stages of plants to decrease the effort for retraining the networks. View Full-Text
Keywords: convolutional neural network; light competition; transfer learning; growth stages; mixed cropping convolutional neural network; light competition; transfer learning; growth stages; mixed cropping
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Munz, S.; Reiser, D. Approach for Image-Based Semantic Segmentation of Canopy Cover in Pea–Oat Intercropping. Agriculture 2020, 10, 354.

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