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René Gislum

Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearch

Targeted fertilization of grass clover leys shows high financial and environmental potentials leading to higher yields of increased quality, while reducing nitrate leaching. To realize the gains, an accurate fertilization map is required, which is closely related to the local composition of plant species in the biomass. In our setup, we utilize a top-down canopy view of the grass clover ley to estimate the composition of the vegetation, and predict the composition of the dry matter of the forage. Using a deep learning approach, the canopy image is automatically pixel wise segmented and classified into white clover, red clover, grass and weeds. While robust grass and clover segmentation has proven to be a difficult task to automate, red and white clover discrimination in images is challenging, even for human experts, due to many visual similarities between the two clover species. Using high-resolution color images with a ground sampling distance of 4 to 6 pixels per mm and data simulation of hierarchical labels, a cascaded convolutional neural network was trained for segmentation and classification. Clover, grass and weeds was automatically segmented and classified with a pixel wise accuracy of 87.3 percent, while red clovers and white clovers could be distinguished automatically with 89.6 percent accuracy. Utilizing the image analysis on 179 images of mixed crop plots of ryegrass, white clover and red clover, demonstrated a linear correlation between the detected clover and clover species fractions in the canopy, and the corresponding compositions in harvested dry matter.
Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Precision Engineering
Number of pages14
Place of publicationMontreal
PublisherInternational Society of Precision Agriculture
Publication year30 Jun 2018
Article number5079
Publication statusPublished - 30 Jun 2018
EventInternational Conference on Precision Agriculture - Montréal, Canada
Duration: 24 Jun 201827 Jun 2018
Conference number: 14
https://www.ispag.org/icpa

Conference

ConferenceInternational Conference on Precision Agriculture
Nummer14
LandCanada
ByMontréal
Periode24/06/201827/06/2018
Internetadresse

    Research areas

  • Precision Agriculture, Targeted Fertilization, Grass-Clover, Deep Learning, Semantic Segmentation, Convolutional Neural Network, Data Simulation, Plant Classification

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