PRE2019 4 Group2
Leighton van Gellecom, Hilde van Esch, Timon Heuwekemeijer, Karla Gloudemans, Tom van Leeuwen
Articles Hilde:
a. Subject: combat of unwanted plants using detection by deep learning
The combat against unwanted potato plants is an intensive and boring task for farmers, which they would gladly leave to robots. Until now this was impossible, since the robots could not distinguish between the potato and beetroot plants. Using deep learning, this has now succeeded with a 96% success rate. A robot was developed which drives on the land and makes pictures, which are sent to a KPN-cloud through 5G. The pictures are then analysed by the deep learning algorithm, and the result is sent back to the robot. This deep learning algorithm was constructed with a dataset of about 5500 labelled pictures of potato and sugar beet plants to train the system. Next, the robot combats the plants that have been detected as the unwanted potato plants using a spraying unit, which is instructed by the system. This development is already a big step forward, but the fault rate is still too large for the system to be put into practice.
Booij, J., Nieuwenhuizen, A., van Boheemen, K., de Vissr, C., Veldhuisen, B., Vroegop, A., ... Ruigrok, T. (2020). 5G Fieldlab Rural Drenthe: duurzame en autonome onkruidbestrijding. (Rapport / Stichting Wageningen Research, Wageningen Plant Research, Business unit Agrosysteemkunde; No. WPR). Wageningen: Stichting Wageningen Research, Wageningen Plant Research, Business unit Agrosysteemkunde. https://doi.org/10.18174/517141
b. Subject: detection of plant disease using deep learning
Potato blackleg is a bacterial disease that can occur in potato plants that causes decay of the plant, and may spread to neighbouring plants if the diseased plant is not taken away. So far, only systems have been devised that were able to detect the disease after harvesting the plants. In this research, a system was created that had a 95% precision rate in detection of healthy and diseased potato plants. This system consisted of a deep learning algorithm, which used a neural network trained by a dataset of 532 labelled images. There is a downside to the system, however, since it was devised, and trained, to detect plants that were separate and do not overlap. In most scenarios, this is not the case. Further developments need to be made to be able to use the system in all scenarios. In addition, it proved to be difficult to gain enough labelled images of the plants.
Afonso, M. V., Blok, P. M., Polder, G., van der Wolf, J. M., & Kamp, J. A. L. M. (2019). Blackleg Detection in Potato Plants using Convolutional Neural Networks. Paper presented at 6th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, AgriControl 2019, Sydney, Australia.
c. Subject: Broad-leaved dock weed plant recognition based on feature extraction and images
Most weed recognition and detection systems designed up to now are specifically designed for a sole purpose or context. Plants are generally considered weeds when they either compete with the crops or are harmful to livestock. Weeds are traditionally mostly battled using pesticides, but this diminishes the quality of the crops. The Broad-leaved dock weed plant is one of the most common grassland weeds, and this research aims to create a general weed recognition system for this weed. The system designed relied on images and feature extraction, instead of the classical choice for neural networks. It had a 89% accuracy.
Kounalakis, T., Triantafyllidis, G. A., & Nalpantidis, L. (2018). Image-based recognition framework for robotic weed control systems. Multimedia Tools and Applications, 77(8), 9567-9594. doi:http://dx.doi.org/10.1007/s11042-017-5337-y
d. Subject: Plant classification of 22 species using feature extraction on the leaves
This paper describes the research of a method to classify plants based on 15 features of their leaves. This yielded a 85% accuracy for classification of 22 species with a training data set of 660 images. The algorithm was based on feature extraction, with the help of the Canny Edge Detector and SVM Classifier.
Salman, A., Semwal, A., Bhatt, U., Thakkar, V.M., "Leaf classification and identification using Canny Edge Detector and SVM classifier," 2017 International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2017, pp. 1-4.
e. Subject: weed classification
A weed is a plant that is unwanted at the place where it grows. This is a rather broad definition, though, and therefore this research was focused on what plants are regarded as weeds among 56 scientists. Again, it was discovered that views greatly differed among the scientists. Therefore it is not possible to clearly classify plants into weeds or non-weeds, since it depends on the views of a person, and the context of the plant.
Perrins, J., Williamson, M., & Fitter, A. (1992). A survey of differing views of weed classification: implications for regulation of introductions. Biological Conservation, 60(1), 47-56.
(Karla: Working on the following articles:)
a. Hemming, J., Blok, P., & Ruizendaal, J. (2018). Precisietechnologie Tuinbouw: PPS Autonoom onkruid verwijderen: Eindrapportage. (Rapport WPR; No. 750). Bleiswijk: Wageningen Plant Research, Business unit Glastuinbouw. https://doi.org/10.18174/442083
b. Hemming, J., Barth, R., & Nieuwenhuizen, A. T. (2013). Automatisch onkruid bestrijden PPL-094 : doorontwikkelen algoritmes voor herkenning onkruid in uien, peen en spinazie. Wageningen: Plant Research International, Business Unit Agrosysteemkunde.
c. Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., . . . Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179-1199. doi:10.1002/rob.21727
d. Duong, L.T., Nguyen, P.T., Sipio, C., Ruscio, D. (2020). Automated fruit recognition using EfficientNet and MixNet. Computers and Electronics in Agriculture, 171. https://doi.org/10.1016/j.compag.2020.105326
e. Carvalho, L., & Von Wangenheim, A. (2019). 3d object recognition and classification: A systematic literature review. Pattern Analysis and Applications, 22(4), 1243-1292. doi:10.1007/s10044-019-00804-4
Articles currently working on Leighton:
a. Piron, A., van der Heijden, F. & Destain, M.F. Weed detection in 3D images. Precision Agric 12, 607–622 (2011). https://doi-org.dianus.libr.tue.nl/10.1007/s11119-010-9205-2
b. Dos Santos Ferreira, A., Matte Freitas, D., Gonçalves da Silva, G., Pistori, H., & Theophilo Folhes, M. (2017). Weed detection in soybean crops using convnets. Computers and Electronics in Agriculture, 143, 314-324. doi:10.1016/j.compag.2017.10.027
c. Alchanatis, V., Ridel, L., Hetzroni, A., & Yaroslavsky, L. (2005). Weed detection in multi-spectral images of cotton fields. Computers and Electronics in Agriculture, 47(3), 243-260. doi:10.1016/j.compag.2004.11.019
d. Yu, J., Schumann, A., Cao, Z., Sharpe, S., & Boyd, N. (2019). Weed detection in perennial ryegrass with deep learning convolutional neural network. Frontiers in Plant Science, 10, 1422-1422. doi:10.3389/fpls.2019.01422
e. Tang, J., Chen, X., Miao, R., & Wang, D. (2016). Weed detection using image processing under different illumination for site-specific areas spraying. Computers and Electronics in Agriculture, 122, 103-111. doi:10.1016/j.compag.2015.12.016
f. https://link-springer-com.dianus.libr.tue.nl/content/pdf/10.1007/s11119-017-9528-3.pdf
Tom's articles:
Two articles investigating the use of specific signalling compounds to mark desired plants, so that weeds can be removed. This created a way of marking the plants with a "machine-readable signal". This could thus be used for automatic classification of plants. According to one of the studies, an accuracy of at least 98% was achieved for detecting weeds and crops. --This is a good state of the art (very recent), but it hasn't actually been automated yet, it just shows the possibility of the method.--
a. doi: https://dx.doi.org/10.1016/j.biosystemseng.2020.02.011 b. doi: https://dx.doi.org/10.1016/j.biosystemseng.2020.02.002
This article describes how they modified farm environment and design to best suit a robot harvester. This took into account what kind of harvesting is possible for a robot, and what is possible for different crops, and then tried to determine how the robot could best do its job.
c. doi: https://dx.doi.org/10.1016/j.biosystemseng.2020.01.021
The next article created a vision and control system that was able to remove most weeds from an area, without explained visual features of crops and weeds. It achieved a crop detection accuracy of 97.8%, and was able to remove 83% of weeds around plants. This seemed to be in a very controlled setting, however, and still works with mainly simple farms.
d. doi: https://doi.org/10.1016/j.biosystemseng.2020.03.022
This last article tries to improve the functioning of vision-based weed control, and does this by taking a slower approach to visual processing and decision-making. It uses multiple cameras, but apparently uses overhead cameras, which aren't suited for all types of crops. It does use 3D vision, so the camera position might be modifiable. --It has been tested on sugar beets, so nothing too special yet. Also this thing is gigantic.--
e. doi: https://doi.org/10.1002/rob.21938
Week 1:
| Name (ID) | Work done |
|---|---|
| Hilde van Esch (1306219) |