Machine Learning for Crop Weed Classification - Revision history

LodewijkVoorhoeve at 11:28, 5 February 2018

2018-02-05T11:28:23Z

2018-02-05T11:27:42Z

2018-02-05T11:27:11Z

2018-02-05T11:26:31Z

2018-02-05T11:21:15Z

‎Results

2018-02-05T11:01:49Z

‎Results

2018-02-05T10:59:17Z

‎Results

2018-02-05T10:51:41Z

‎Results

2018-01-29T15:14:00Z

2018-01-29T14:09:37Z

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+| image=[[File:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png|200px]]
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+| image=[[File:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png|150px|thumb]]
 }}

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 | level=Ms
 | type=Thesis
-| status=Inactive
+| status=Closed
-| image=File:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png
+| image=[[File:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png]]
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 | level=Ms
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-| status=Active
+| status=Inactive
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+| image=File:Bonirob 2016-05-23-10-37-10 0 frame23 GroundTruth color.png
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← Older revision		Revision as of 11:21, 5 February 2018
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	[[File:crop-weed-soil_comparison.png\|200px\|thumb\|right\|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.]]		[[File:crop-weed-soil_comparison.png\|200px\|thumb\|right\|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]]

← Older revision		Revision as of 11:01, 5 February 2018
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	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.		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.

−	[[File:crop-weed-soil_comparison.png\|200px\|thumb\|~~left~~\|~~alt text~~]]	+	[[File:crop-weed-soil_comparison.png\|200px\|thumb\|right\|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.]]

← Older revision		Revision as of 10:59, 5 February 2018
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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.		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.
		+
		+	[[File:crop-weed-soil_comparison.png\|200px\|thumb\|left\|alt text]]

← Older revision		Revision as of 10:51, 5 February 2018
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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.

@@ Line 36: / Line 36: @@
 === Methods ===
+===== Data =====
 === Results ===

← Older revision		Revision as of 14:09, 29 January 2018
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	This project is related to software developing so there are no dangerous activities		This project is related to software developing so there are no dangerous activities
		+
		+	=== Methods ===
		+
		+
		+
		+	=== Results ===