• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• Smart Media Journal
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• v.7 no.3
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• pp.16-21
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• 2018

This paper presents the design and implementation of a deep learning-based automated weed detector on onion fields. The system is based on a Convolutional Neural Network that specifically selects proposed regions. The detector initiates training with a dataset taken from agricultural onion fields, after which candidate regions with very high probability of suspicion are considered weeds. Non-maximum suppression helps preserving the less overlapped bounding boxes. The dataset collected from different onion farms is evaluated with the proposed classifier. Classification accuracy is about 99% for the dataset, indicating the proposed method's superior performance with regard to weed detection on the onion fields.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.159-164
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• 2019

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.

• Korean Journal of Weed Science
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• v.32 no.1
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• pp.10-16
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• 2012

This study was carried out to investigate the agronomic traits, comparison of weed characteristics and possibility of gene flow in 'vitamin A enforced GM rice' and the donor plant, 'Nagdong'. The GM rice was not significantly different agronomic traits compared to the donor plant, Nagdong. Weed population changes were investigated in the cultivation of the GM rice and the donor plant, Nagdong. Dominant weed species and their dry matter did not show the difference between GM rice and the donor plant, Nagdong in macro-GM crop field. Dominant weed species with the GM rice and the donor plant, Nagdong were Monochoria vaginalis, followed by Eleocharis kuroguwai, Echinochloa crus-galli and Lindernia procumbens. The detection of gene from the GM rice was done using PCR, gene flow can't be detected by weed species. Results of this study on the agronomic traits, weed characteristics and possibility of gene flow has elucidated that GM rice might not be different from the donor plant, Nagdong.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• Journal of Biosystems Engineering
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• v.31 no.1 s.114
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• pp.59-64
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• 2006

Application of herbicide for rice cropping is inevitable but notorious for its side effect of environmental pollution. Precision fanning will be one of important tools for the least input and sustainable fanning and could be achieved by implementation of the variable rating technology. If a device to detect weeds in rice field is available, herbicide could be applied only to the places where it is needed by the manner of the variable rating technology. The study was carried out to develop an algorithm of image processing to detect weeds in rice field using a machine vision system of multi-spectral digital images. A series of multi-spectral rice field picture of 560, 680 and 800 nm of center wavelengths were acquired from the 27th day to the 39th day after transplanting in the ineffective tillering stage of a rice growing period. A discrimination model to distinguish pixels of weeds from those of rice plant and weed image was developed. The model was proved as having accuracies of 83.6% and 58.9% for identifying the rice plant and the weed, respectively. The model was used in the algorithm to differentiate weed images from mingled images of rice plant and weed in a frame of rice field picture. The developed algorithm was tested with the acquired rice field pictures and resulted that 82.7%, 11.9% and 5.4% of weeds in the pictures were noted as the correctly detected, the undetected and the misclassified as rice, respectively, and 81.9% and 18.0% of rice plants in the pictures were marked as the correctly detected and the misclassified as weed, respectively.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.143-150
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• 2002

Several researches for site-specific weed control have tried to increase accuracy of weed detection with machine vision technique. However, there is a problem which needs substantial time to perform site-specific spraying. Therefore, new technology for real-time precision spraying system is needed. This research was executed to develope the new technology to estimate weed density and size in real time, and to conduct a real-time site-specific spraying. It would effectively reduce herbicide amounts applied for a crop field. The real-time precision spraying system consisted of a Differential Global Positioning System (DGPS) with an error of 2 cm, a machine vision system, a geomagnetic sensor for correction of view point of CCD camera and an automatic sprayer with separately controlled nozzle. The weed density was calculated with comparison between position information and a pre-designed electronic map. The position information was obtained in real time using the DGPS and the machine vision. The electronic map contained a position database of crops automatically constructed when seeding. The developed system was tested on an experimental field of Seoul National University. Success rate of the spraying was about 61%.

• The Plant Pathology Journal
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• v.40 no.3
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• pp.310-321
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• 2024

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• The Plant Pathology Journal
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• v.15 no.3
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• pp.137-143
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• 1999

A novel chromosomal gene tagging technique using a specific fragment of the fatty acid desaturase-like open reading frame (des-like ORF) from the tox-argK gene cluster of Pseudomonas syringae pv. phaseolicola was developed to identify Xanthomonas spp.released into the environment as biocontrol agents. X. campestris pv. convolvuli FB-635, a pathogen of Convolvulus arvensis L., (bindweed), was chosen as the organism in which to develop and test the system. A 0.52 kb DES fragment amplified from P. syringae pv. phaseolicola C-199 was inserted into pGX15, a cosmid clone containing a 10.3 kb Eco RI-HindIII fragment derived from the xanthomonadin biosynthetic gene cluster contained in plasmid pIG102, to create a pigG::DES insertion. The 10.8 kb EcoRI-BamHI fragment carrying the pigG:: DES insertion was cloned into pLAFR3 to generate pLXP22. pLXP22 was then conjugated into X. campestris pv. convolvuli FB-635 and the pigG::DES insertion integrated into the bacterial chromosome by marker exchange. Rifampicin resistant, tetracycline sensitive, starch hydrolyzing, white colonies were used to differentiate the marked strain from yellow pigmented wild-type ones. PCR primers specific for the unique DES fragment were used for direct detection of the marked strain. Result showed the marked strain could be detected at very low levels even in the presence of high levels of other closely related or competitive bacteria. This PCR-based DES-tagging system provides a rapid and specific tool for directly monitoring the dispersal and persistence of Xanthomonas spp.released into the environment.