Edge detection for weed recognition in lawns

[EN] The rapid propagation of weeds is a major issue for turfgrass management (both ornamental and sports turf). While pesticides can ensure weed eradication, they pose a risk to human health and the environment. In this context, the early detection of weeds can allow a dramatic reduction in the amount of pesticide required. Here we present the use of edge detection techniques to identify the presence of these invasive plants in ornamental lawns and sports turf. Regarding the former, images from small experimental plots in the facilities of IMIDRA were used while images for the latter were taken on a golf course. Up to 12 different filters for edge detection were tested on the images collected. Aggregation techniques, with a range of cell values, were applied to the results of the three most effective filters (sharpening (I), sharpening (II), and Laplacian) to minimise the number of false positives. After the tests with different cell sizes, two filters were selected for more in-depth analysis. Box plots were selected to define the best cell size and identify the filter with the best performance. The sharpening (I) filter and the aggregation technique with the minimum value and a cell size of 10 offered the best results. Finally, we determined the most appropriate threshold value on the basis of the number of false positives, false negatives, and derived indexes (Precision, Recall, and F1-Score). A threshold of 78 gave the best performance. The results achieved with this methodology differed slightly between ornamental and sports turf.This work was partially funded by the Conselleria de Educacion, Cultura y Deporte through "Subvenciones para la contratacion de personal investigador en fase postdoctoral", grant number APOSTD/2019/04, by the European Union through ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by the European Union with the "Fondo Europeo Agricola de Desarrollo Rural (ERDF) - Europa invierte en zonas rurales", the MAPAMA, and Comunidad de Madrid with the IMIDRA, through the "PDR-CM 2014-2020" project number PDR18-XEROCESPED.Parra, L.; Marin, J.; Yousfi, S.; Rincón, G.; Mauri Ablanque, PV.; Lloret, J. (2020). Edge detection for weed recognition in lawns. Computers and Electronics in Agriculture. 176:1-13. https://doi.org/10.1016/j.compag.2020.10568411317

Comparison of Single Image Processing Techniques and Their Combination for Detection of Weed in Lawns

[EN] The detection of weeds in lawns is important due to the different negative effects of its presence. Those effects include a lack of uniformity and competition for the resources. If the weeds are detected early the phytosanitary treatment, which includes the use of toxic substances, will be more effective and will be applied to a smaller surface. In this paper, we propose the use of image processing techniques for weed detection in urban lawns. The proposed methodology is based on simple techniques in order to ensure that they can be applied in-situ. We propose two techniques, one of them is based on the mathematical combination of the red, green and blue bands of an image. In this case, two mathematical operations are proposed to detect the presence of weeds, according to the different colorations of plants. On the other hand, we proposed the use of edge detection techniques to differentiate the surface covered by grass from the surface covered by weeds. In this case, we compared 12 different filters and their combinations. The best results were obtained with the Laplacian filter. Moreover, we proposed to use pre-processing and post-processing operations to remove the soil and to aggregate the data with the aim of reducing the number of false positives. Finally, we compared both methods and their combination. Our results show that both methods are promising, and its combination reduces the number of false positives (0 false positives in the 4 evaluated images) ensuring the detection of all weeds.This work is partially found by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by the European Union with the "Fondo Europeo Agrícola de Desarrollo Rural (FEADER) - Europa invierte en zonas rurales", the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020 project number PDR18-XEROCESPED.Parra-Boronat, L.; Parra-Boronat, M.; Torices, V.; Marín, J.; Mauri, PV.; Lloret, J. (2019). Comparison of Single Image Processing Techniques and Their Combination for Detection of Weed in Lawns. International Journal On Advances in Intelligent Systems. 12(3-4):177-190. http://hdl.handle.net/10251/158241S177190123-

Weed Recognition in Agriculture: A Mask R-CNN Approach

Recent interdisciplinary collaboration on deep learning has led to a growing interest in its application in the agriculture domain. Weed control and management are some of the crucial tasks in agriculture to maintain high crop productivity. The inception phase of weed control and management is to successfully recognize the weed plants, followed by providing a suitable management plan. Due to the complexities in agriculture images, such as similar colour and texture, we need to incorporate a deep neural network that uses pixel-wise grouping for identifying the plant species. In this thesis, we analysed the performance of one of the most popular deep neural networks aimed to solve the instance segmentation (pixel-wise analysis) problems: Mask R-CNN, for weed plant recognition (detection and classification) using field images and aerial images. We have used Mask R-CNN to recognize the crop plants and weed plants using the Crop/Weed Field Image Dataset (CWFID) for the field image study. However, the CWFID\u27s limitations are that it identifies all weed plants as a single class and all of the crop plants are from a single organic carrot field. We have created a synthetic dataset with 80 weed plant species to tackle this problem and tested it with Mask R-CNN to expand our study. Throughout this thesis, we predominantly focused on detecting one specific invasive weed type called Persicaria Perfoliata or Mile-A-Minute (MAM) for our aerial image study. In general, supervised model outcomes are slow to aerial images, primarily due to large image size and scarcity of well-annotated datasets, making it relatively harder to recognize the species from higher altitudes. We propose a three-level (leaves, trees, forest) hierarchy to recognize the species using Unmanned Aerial Vehicles(UAVs) to address this issue. To create a dataset that resembles weed clusters similar to MAM, we have used a localized style transfer technique to transfer the style from the available MAM images to a portion of the aerial images\u27 content using VGG-19 architecture. We have also generated another dataset at a relatively low altitude and tested it with Mask R-CNN and reached ~92% AP50 using these low-altitude resized images

Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

d Weed Recognition in Agriculture Using Mask R-CNN

Recent cooperation on deep learning has piqued the curiosity of those interested to utilise the techniques in agriculture. Weed management system is significant in agriculture that must be completed to improve crop production. The first step in weed management is to accurately classify the weeds and crops with an effective management strategy. Due to the enormous complexities in agricultural images, such as identical colour and texture, a deep neural network with pixel-wise grouping must be used to identify the plant type. The effectiveness of one of the most famous deep neural networks is examined in this paper to tackle the instance segmentation problems. Using field photos, Mask R-CNN is used to recognise weed plants (detection and classification). The dataset, which contains weeds and plants, is used to train a Mask R-CNN computer vision framework to classify and locate unique occurrences of weeds among plants. The dataset was trained on the MS COCO dataset, and the model was tailored to our classification purpose via transfer learning. Some well-reported problems in developing a suitable model are instance occlusion and the major resemblance between weeds and crops. Mask�RCNN is built on the FPN and the ResNet101 backbone. After the field images are tested on the pre-trained Mask R-CNN model, Mask R-CNN will give a class label and a bounding box offset for each weed and crop recognised. Moreover, the recognised weeds and crops will be given an object mask. Using the Mask R-CNN, the system can effectively perform instance segmentation on the images of weeds and crops with higher accuracy

Publications (Missouri Cooperative Extension Service, 1988)

This is a list of popular University of Missouri Publications.Revised 4/8

A Study on Hyper Spectral Remote Sensing Pest Management

Designing innovative combination of techniques to improve the sustainability of cropping system is a major challenge in many regions of the world. Long-term cropping systems research is important in order to reduce production costs, to control crop pests, and to optimize the sustainability of agro-ecosystems. Research into vegetative spectral reflectance can help us gain a better understanding of the physical, physiological and chemical processes in plants due to pest and disease attack and to detect the resulting biotic stress. This has important implications to effective pest management. Pest surveillance programs such as field scouting are often expensive, time consuming, laborious and prone to error. As remote sensing gives a synoptic view of the area in a non-destructive and non-invasive way, this technology could be effective and provide timely information on spatial variability of pest damage over a large area. In this paper to study management of water, nutrients, and pests in agricultural crops and assesses the role of hyperspectral remote sensing in yield prediction and also remote sensing can guide scouting efforts and crop protection advisory in a more precise and effective manner in the field of pest management

Development of Vegetation Mapping with Deep Convolutional Neural Network

The Precision Agriculture plays a crucial part in the agricultural industry about improving the decision-making process. It aims to optimally allocate the resources to maintain the sustainable productivity of farmland and reduce the use of chemical compounds. [17] However, the on-site inspection of vegetations often falls to researchers’ trained eye and experience, when it deals with the identification of the non-crop vegetations. Deep Convolution Neural Network (CNN) can be deployed to mitigate the cost of manual classification. Although CNN outperforms the other traditional classifiers, such as Support Vector Machine, it is still in question whether CNN can be deployable in an industrial environment. In this paper, I conducted a study on the feasibility of CNN for Vegetation Mapping on lawn inspection for weeds. I want to study the possibility of expanding the concept to the on-site, near real-time, crop site inspections, by evaluating the generated results

Quantifying the Production of Fruit-Bearing Trees Using Image Processing Techniques

[EN] In recent years, the growth rate of world agricultural production and crop yields have decreased. Crop irrigation becomes essential in very dry areas and where rainfall is scarce, as in Egypt. Persimmon needs low humidity to obtain an optimal crop. This article proposes the monitoring of its performance, in order to regulate the amount of water needed for each tree at any time. In our work we present a technique that consists of obtaining images of some of the trees with fruit, which are subsequently treated, to obtain reliable harvest data. This technique allows us to have control and predictions of the harvest. Also, we present the results obtained in a first trial, through which we demonstrate the feasibility of using the system to meet the objectives set. We use 5 different trees in our experiment. Their fruit production is different (between 20 and 47kg of fruit). The correlation coefficient of the obtained regression model is 0.97.This work has been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, and by the Cooperativa Agrícola Sant Bernat Coop.V.García, L.; Parra-Boronat, L.; Basterrechea-Chertudi, DA.; Jimenez, JM.; Rocher-Morant, J.; Parra-Boronat, M.; García-Navas, JL.... (2019). Quantifying the Production of Fruit-Bearing Trees Using Image Processing Techniques. IARIA XPS Press. 14-19. http://hdl.handle.net/10251/180619S141