Difference between revisions of "Machine Learning for Crop Weed Classification"
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=== Results === | === Results === | ||
| + | With the modified code, both the CWFID and SB2016 datasets were analysed. For SB2016, a comparison was made with the paper by Milioto, Lottes and Stachniss. The comparison is shown in the image on the right, where the baseline classifiers RFC and SVM perform better than the SegNet architecture of Milioto, Lottes and Stachniss on the crop, but worse on the weed and comparable on the soil. | ||
Revision as of 11:51, 5 February 2018
Machine Learning for Crop Weed Image Classification
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| Short Description: | Automatic detection of crops and weeds by using image data |
| Coordinator: | |
| Tutor: | MatteoMatteucci (matteo.matteucci@polimi.it) |
| Collaborator: | |
| Students: | LodewijkVoorhoeve (lodewijk.voorhoeve@wur.nl) |
| Research Area: | Machine Learning |
| Research Topic: | Image Classification |
| Start: | 2017/09/4 |
| End: | 2018/01/31 |
| Status: | Active |
| Level: | Ms |
| Type: | Thesis |
Contents
Project short description
The aim of this project is to transfer the work of Stefano Cereda on crop/weed detection to TensorFlow and to train the model with new data.
Dates
Start date: 2017/09/04
End date: 2018/01/30
Project head(s)
Students currently working on the project
Laboratory work and risk analysis
This project is related to software developing so there are no dangerous activities
Methods
Crop and weed classification can be accomplished by analyzing an image with the use of Machine Learning. Supervised Machine Learning makes use of data (for example: an image) and an annotation file (an image of the same size, with colours demarcating the area that belongs to a certain class). Therefore, the basis of this project is the availability of data.
Data
Two publicly available datasets are used: Crop/Weed Field Image Dataset (CWFID) and Sugar Beets 2016 (SB2016).
Results
With the modified code, both the CWFID and SB2016 datasets were analysed. For SB2016, a comparison was made with the paper by Milioto, Lottes and Stachniss. The comparison is shown in the image on the right, where the baseline classifiers RFC and SVM perform better than the SegNet architecture of Milioto, Lottes and Stachniss on the crop, but worse on the weed and comparable on the soil.
