Machine Learning for Crop Weed Classification

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Machine Learning for Crop Weed Image Classification
[[Image:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png|200px|Image of the project Machine Learning for Crop Weed Classification]]
[[prjImage::Image:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png|]]
Short Description: Automatic detection of crops and weeds by using image data
Tutor: MatteoMatteucci (
Students: LodewijkVoorhoeve (
Research Area: Machine Learning
Research Topic: Image Classification
Start: 2017/09/4
End: 2018/01/31
Status: Closed
Level: Ms
Type: Thesis

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.


Start date: 2017/09/04

End date: 2018/01/30

Project head(s)


Students currently working on the project

Lodewijk Voorhoeve

Laboratory work and risk analysis

This project is related to software developing so there are no dangerous activities


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.


Two publicly available datasets are used: Crop/Weed Field Image Dataset (CWFID) and Sugar Beets 2016 (SB2016).


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.

Comparison of crop, weed and soil with the metrics of IoU, Precision and Recall. The baseline classifiers RFC and SVM outperform the SegNet neural network architecture on crop, but perform worse on weed and similar on soil.

The final report can be found here Media:Final_Internship_Report_-_Machine_Learning_for_Crop_and_Weed_Classification_1.0.pdf