DeepSeedling

Deep convolutional network and Kalman filter for plant seedling detection and counting in the field

Plant population density is an important factor for agricultural production systems due to its substan- tial influence on crop yield and quality. Traditionally, plant population density is estimated by using either field assess- ment or a germination-test-based approach. These approaches can be laborious and inaccurate. Recent advances in deep learning provide new tools to solve challenging computer vision tasks such as object detection, which can be used for detecting and counting plant seedlings in the field. The goal of this study was to develop a deep-learning- based approach to count plant seedlings in the field.

Fig. 1  Cotton plant seedlings and weeds detected in representative images of the SeedlingAll testing set by the Faster RCNN model that was trained using the SeedlingAll training set.

Award(s)

Publication(s)
Jiang, Y., Li, C., Paterson, A. H., & Robertson, J. S. (2019). DeepSeedling: deep convolutional network and Kalman filter for plant seedling detection and counting in the field. Plant Methods15(1). doi: 10.1186/s13007-019-0528-3

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