Journal articles: 'Weed identification'

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Bushra Idrees. "Weed Identification Methodology by using Transfer Learning." Lahore Garrison University Research Journal of Computer Science and Information Technology 5, no. 2 (June 21, 2021): 28–39. http://dx.doi.org/10.54692/lgurjcsit.2021.0502206.

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From recent past years, Weed identification remained a hot topic for researchers. Majority of work focused on the detection of weed but we are trying to identify the weed via weed name. The unrivaled successes of deep learning make the researchers able to evaluate different weed species in the complex rangeland climate. Nowadays, with an increasing population, farming productivity needs to be increased a lot to meet the demand for accurate weed detection. Increased demand for an increase in the use of herbicides, resulting in environmental harm. In this research work, the picture of weed helps to detect and differentiate as per area, and its name. The main aim of this research is the identification of weed so that fewer herbicides can use. This research work will contribute toreducing the higher use of herbicides by helping clear identification of weed names through its features. We use transfer learning in machine learning. The deep Weeds dataset is used for the evaluation. For this, we use the deep learning model ResNet50 to get better results. The Deep Weeds dataset contains 17,509 images that are label and eight nationally recognized species of weed belonged to 8 across northern Australia locations. This paper declares a baseline for classification performance on the dataset of weed while utilizing the deep learning model ResNet-50 and it is a benchmark too. Deep learning model ResNet-50 attained an average accuracy classification of 96.16. The findings are high enough to make effective use of weed control methods in Pakistan for futurefield implementation. The results confirm that our System offers more effective Weed recognition than many other systems.

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Werle, Isabel Schlegel, Edicarlos Castro, Carolina Pucci, Bhawna Soni Chakraborty, Shaun Broderick, and Te Ming Tseng. "Identification of Weed-Suppressive Tomato Cultivars for Weed Management." Plants 11, no. 3 (February 2, 2022): 411. http://dx.doi.org/10.3390/plants11030411.

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Weed-suppressive crop cultivars are a potentially attractive option in weed management strategies (IWM). A greenhouse study was conducted at the R. R. Foil Plant Science Research Center, Starkville, MS, to assess the potential weed-suppressive ability of 17 tomato cultivars against Palmer amaranth (Amaranthus palmeri S. Wats), yellow nutsedge (Cyperus esculentus L.), and large crabgrass (Digitaria sanguinalis L.). The experiment was a completely randomized design, with four replications, and was repeated twice. The height, chlorophyll, and dry weight biomass of the weeds were measured 28 days after sowing. Weed suppression varied greatly among tomato cultivars. The most significant effect of tomato interference was recorded on Palmer amaranth, and the least reduction was observed with yellow nutsedge plants. Cultivars 15 and 41 reduced Palmer amaranth height and biomass by about 45 and 80%, respectively, while cultivar 38 reduced 60% of the chlorophyll percentage. Large crabgrass plants were 35% shorter in the presence of cultivar 38 and had a biomass reduction of 35% in the presence of cultivar 38. Under tomato interference, a minimal effect was observed in chlorophyll, height, and biomass of yellow nutsedge seedlings. Factoring all parameters evaluated, cultivars 38 and 33 were most suppressive against Palmer amaranth and large crabgrass.

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Murdock, Edward C., Judy A. Alden, DeWitt T. Gooden, and Joe E. Toler. "Weed identification field training demonstrations." Journal of Agronomic Education 15, no. 1 (March 1986): 37–40. http://dx.doi.org/10.2134/jae1986.0037.

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Schulthess, Urs, Kris Schroeder, Ahmed Kamel, AbdElGhani M. AbdElGhani, El‐Hassanein E. Hassanein, Shaban Sh AbdElHady, AbdElMaboud AbdElShafi, Joe T. Ritchie, Richard W. Ward, and Jon Sticklen. "NEPER‐Weed: A Picture‐Based Expert System for Weed Identification." Agronomy Journal 88, no. 3 (May 1996): 423–27. http://dx.doi.org/10.2134/agronj1996.00021962008800030010x.

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Zahoor, Saniya, and Shabir A.Sofi. "Weed Identification in Crop Field Using CNN." Journal of University of Shanghai for Science and Technology 23, no. 10 (October 1, 2021): 15–21. http://dx.doi.org/10.51201/jusst/21/09697.

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Since the ages agriculture has remained as the backbone of economies especially developing countries like ours, where population is growing rapidly being second most populated country in the world, food demands are increasing so, farmers need to maximize their productivity. Weed is one of the enemies to farmer’s crop which competes with the crop for nutrients and sometimes hinders the growth of crop. Weed can cause loss of production ranging from 10 to 100%. There has been research on the use of many CNN models for weed identification. This paper presents a classification model to distinguish between weed and crop images and it classifies 12 species of weeds and crops. The proposed model achieves 96.45% of accuracy during training and of 90.08% during validation and testing.

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Lindquist, John L., Peter K. Fay, and James E. Nelson. "Teaching Weed Identification at Twenty U.S. Universities." Weed Technology 3, no. 1 (March 1989): 186–88. http://dx.doi.org/10.1017/s0890037x00031596.

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The methods used to teach weed identification at 20 U.S. universities were obtained for comparison through a telephone survey in December, 1986, and January, 1987. Weed identification is taught as a portion (30%) of the laboratory section in introductory weed science courses. Only five have a separate weed identification course. Field trips frequently are used to teach weed identification. Students must learn from 50 to 125 weed species with some seedling identification. Pressed plant collections of approximately 50 weed species normally are required. Most instructors strongly suggest using live plants and repetition for long-term learning.

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Putniece, Gundega, Ingrīda Augšpole, and Inta Romanova. "Population of Weeds in a Plantation of Red Raspberries (Rubus Idaeus L.)." Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences. 76, no. 4 (August 1, 2022): 551–54. http://dx.doi.org/10.2478/prolas-2022-0085.

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Abstract Data from monitoring of weed populations are relevant for successful integrated weed management. The purpose of this experiment was to compare the diversity of weed species in red raspberry plantation rows. The red raspberry plantation was established in August 2019. The plantation is located in the south part of Latvia, Zemgale region (N56°33’29.5302”, E23°46’26.04”). The red raspberry cultivars ‘Daiga’, ‘Shahrazada’, ‘Norna’, and ‘Polana’ were grown in the plantation. The bushes were spaced at 0.6 m in rows and 3 m between rows in a plot with size 0.51 ha. Weed infestation in the red raspberry plantation was determined by using the counting method. The counting of weeds and identification of weed species were done three times during the vegetation at the 16th,22nd, and 30th week of 2021. In total, 34 weed species, including 18 annual and 16 perennial weeds were present in the red raspberry plantation. Poa annua (5–60 plants per m-2) and Elytrigia repens (6–393 plants m−2) were found as dominant weeds. During experiment there were differences in weed populations and density between red raspberry cultivars. The cultivar ‘Norna’ was dominated by perennial weeds, while ‘Polana’ by annual weeds.

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Granitto, Pablo M., Hugo D. Navone, Pablo F. Verdes, and H. A. Ceccatto. "Weed seeds identification by machine vision." Computers and Electronics in Agriculture 33, no. 2 (February 2002): 91–103. http://dx.doi.org/10.1016/s0168-1699(02)00004-2.

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Gibson, Kevin D., Richard Dirks, Case R. Medlin, and Loree Johnston. "Detection of Weed Species in Soybean Using Multispectral Digital Images." Weed Technology 18, no. 3 (September 2004): 742–49. http://dx.doi.org/10.1614/wt-03-170r1.

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The objective of this research was to assess the accuracy of remote sensing for detecting weed species in soybean based on two primary criteria: the presence or absence of weeds and the identification of individual weed species. Treatments included weeds (giant foxtail and velvetleaf) grown in monoculture or interseeded with soybean, bare ground, and weed-free soybean. Aerial multispectral digital images were collected at or near soybean canopy closure from two field sites in 2001. Weedy pixels (1.3 m2) were separated from weed-free soybean and bare ground with no more than 11% error, depending on the site. However, the classification of weed species varied between sites. At one site, velvetleaf and giant foxtail were classified with no more than 17% error, when monoculture and interseeded plots were combined. However, classification errors were as high as 39% for velvetleaf and 17% for giant foxtail at the other site. Our results support the idea that remote sensing has potential for weed detection in soybean, particularly when weed management systems do not require differentiation among weed species. Additional research is needed to characterize the effect of weed density or cover and crop–weed phenology on classification accuracies.

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Elstone, Lydia, Kin Yau How, Samuel Brodie, Muhammad Zulfahmi Ghazali, William P. Heath, and Bruce Grieve. "High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding." Sensors 20, no. 2 (January 14, 2020): 455. http://dx.doi.org/10.3390/s20020455.

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Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

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Zhang, Hui, Zhi Wang, Yufeng Guo, Ye Ma, Wenkai Cao, Dexin Chen, Shangbin Yang, and Rui Gao. "Weed Detection in Peanut Fields Based on Machine Vision." Agriculture 12, no. 10 (September 24, 2022): 1541. http://dx.doi.org/10.3390/agriculture12101541.

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The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes a weed identification model named EM-YOLOv4-Tiny incorporating multiscale detection and attention mechanisms based on YOLOv4-Tiny. Firstly, an Efficient Channel Attention (ECA) module is added to the Feature Pyramid Network (FPN) of YOLOv4-Tiny to improve the recognition of small target weeds by using the detailed information of shallow features. Secondly, the soft Non-Maximum Suppression (soft-NMS) is used in the output prediction layer to filter the best prediction frames to avoid the problem of missed weed detection caused by overlapping anchor frames. Finally, the Complete Intersection over Union (CIoU) loss is used to replace the original Intersection over Union (IoU) loss so that the model can reach the convergence state faster. The experimental results show that the EM-YOLOv4-Tiny network is 28.7 M in size and takes 10.4 ms to detect a single image, which meets the requirement of real-time weed detection. Meanwhile, the mAP on the test dataset reached 94.54%, which is 6.83%, 4.78%, 6.76%, 4.84%, and 9.64% higher compared with YOLOv4-Tiny, YOLOv4, YOLOv5s, Swin-Transformer, and Faster-RCNN, respectively. The method has much reference value for solving the problem of fast and accurate weed identification in peanut fields.

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Swain, K. C., R. Moitra, and Q. U. Zaman. "Weed Identification using Ultrasonic Sensor in Labview." International Journal of Bio-resource and Stress Management 6, no. 1 (2015): 151. http://dx.doi.org/10.5958/0976-4038.2015.00026.3.

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ZHANG, Naiqian, Ning WANG, Mohammed S. ELFAKI, Chatchai CHAISATTAPAGON, and Ge FAN. "Weed Identification Using Imaging Technology and Spectroscopy." Environment Control in Biology 44, no. 3 (2006): 161–71. http://dx.doi.org/10.2525/ecb.44.161.

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Maniak, S., P. Á. Mesterházi, M. Neményi, and G. Milics. "MACHINE VISION FOR ON-LINE WEED IDENTIFICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 80–85. http://dx.doi.org/10.3182/20050703-6-cz-1902.02104.

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Rahman, Mahbubur, Brenna Blackwell, Nilanjan Banerjee, and Dharmendra Saraswat. "Smartphone-based hierarchical crowdsourcing for weed identification." Computers and Electronics in Agriculture 113 (April 2015): 14–23. http://dx.doi.org/10.1016/j.compag.2014.12.012.

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Elakya, R., U. Vignesh, P. Valarmathi, N. Chithra, and S. Sigappi. "A Novel Approach for Identification of Weeds in Paddy By using Deep Learning Techniques." International Journal of Electrical and Electronics Research 10, no. 4 (December 30, 2022): 832–36. http://dx.doi.org/10.37391/ijeer.100412.

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Weed is an unwanted plant which is grown in agriculture land. The land which is not cultivated will be fully covered by Weeds. Management of weed is the major concern for farmer because the weed will reduce the crop production quantity. There are many methods to control the weeds, one of those methods is manual plucking which is expensive because it takes more time, consumes human work. Second is by applying any chemicals suggested by external experts. This may cause damage to the crop which is cultivated. Identifying weeds in early stage of crop growth and destroying them through proper method is most important for increasing the crop production. We proposed an efficient method for identifying and classifying weed in paddy field by using Deep learning-based computer vision techniques. We applied Semantic Segmentation model for classifying weeds in agriculture land. We trained our model with SegNet with different batch size of 16,32,64 and obtained a highest accuracy of 94.223 for dropout value 0.1 and batch size set to 32.

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Li, Yang-Han. "The Development and Future Trend of Weed Science in Mainland China." Weed Technology 1, no. 3 (July 1987): 259–64. http://dx.doi.org/10.1017/s0890037x00029663.

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Professor Li has taught botany since 1940 at University of Nanking, Nanking, China, and weed science at Nanjing Agricultural University, Nanjing, China, since 1964. He developed and taught the school's first weed science course and he presently teaches botany, crop plant anatomy, and weed identification and control. Under his direction, programs in weed quarantine and biological weed control have been instituted and supported by the Ministry of Agriculture and the Bureau of Education (present name: Ministry of Agriculture, Animal Husbandry and Fishery). One of his prime targets has been the study of darnel and the biological control of dodder on soybeans. Professor Li is the supervisor of several graduate students for the Masters and Ph.D. degrees. While at Nanjing, he also has directed the establishment of the most complete herbarium of weeds and weed seeds in China. Currently, Professor Li is editing Weed Flora of China. In addition to publishing many weed texts and teaching short courses on weed identification and control within China, he is a member of the FAO Panel of Experts on Improved Weed Management.

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Xu, Yanlei, Run He, Zongmei Gao, Chenxiao Li, Yuting Zhai, and Yubin Jiao. "Weed Density Detection Method Based on Absolute Feature Corner Points in Field." Agronomy 10, no. 1 (January 13, 2020): 113. http://dx.doi.org/10.3390/agronomy10010113.

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Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

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Santiago, Wesley E., Neucimar J. Leite, Bárbara J. Teruel, Manoj Karkee, and Carlos AM Azania. "Evaluation of bag-of-features (BoF) technique for weed management in sugarcane production." November 2019, no. 13(11):2019 (November 20, 2019): 1819–25. http://dx.doi.org/10.21475/ajcs.19.13.11.p1838.

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Weeds interfere in agricultural production, causing a reduction in crop yields and quality. The identification of weed species and the level of infestation is very important for the definition of appropriate management strategies. This is especially true for sugarcane, which is widely produced around the world. The present study has sought to develop and evaluate the performance of the Bag-of-Features (BoF) approach for use as a tool to aid decision-making in weed management in sugarcane production. The support vector machine to build a mathematical model of rank consisted of 30553 25x25-pixel images. Statistical analysis demonstrated the efficacy of the proposed method in the identification and classification of crops and weeds, with an accuracy of 71.6% and a Kappa index of 0.43. Moreover, even under conditions of high weed density and large numbers of overlapping and/or occluded leaves, weeds could be distinguished from crops This study clearly shows that the system can provide important subsidies for the formulation of strategies for weed management, especially in sugarcane, for which the timing of weed control is crucial.

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Lewthwaite, S. L., and C. M. Triggs. "Identification of paraquatresistant Solanum nigrum and S americanum biotypes." New Zealand Plant Protection 62 (August 1, 2009): 349–55. http://dx.doi.org/10.30843/nzpp.2009.62.4811.

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Sweetpotato (Ipomoea batatas (L) Lam) plants are highly susceptible to weed competition during their early field establishment period For a long time the contact herbicide paraquat has been widely used within the sweetpotato production system often as the only herbicide applied to control weeds that emerge within the crop A low rate of paraquat is applied repeatedly over the crop each season eliminating seedling weeds at a very young stage until the crop canopy provides adequate competition However the application of paraquat to selectively destroy seedling weeds has inadvertently selected for paraquatresistant weed biotypes This study confirms growers field observations that populations of paraquatresistant solanaceous weeds are now common identifying them as Solanum nigrum L and S americanum Mill Paraquat cannot be used selectively when these resistant biotypes are present as the concentrations required to control the weed species would be lethal to the crop itself

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Norris, Robert F. "Weed Science Society of America weed biology survey." Weed Science 45, no. 3 (June 1997): 343–48. http://dx.doi.org/10.1017/s0043174500092961.

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WSSA members were surveyed in 1993–1994 to determine their perceptions of the contribution of weed biology to weed management. A questionnaire was included in the society newsletter, from which 152 responses were returned by mail or collected at the 1994 annual meeting. Over half the respondents felt that the overall contribution of weed biology to weed management had been moderate to high. Aspects of population dynamics and competition emerged as the areas that respondents felt should have the greatest impact on weed management in the future. The areas of computer modeling, interactions between weeds and other pests, and seedbank dynamics were predicted to show the greatest increases in importance in the future. The relative importance of taxonomy and weed identification was expected to decrease. Allelopathy, morphology and anatomy, and genetics and evolution were considered least likely to be important to weed management.

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Slaughter, David C., D. Ken Giles, Steven A. Fennimore, and Richard F. Smith. "Multispectral Machine Vision Identification of Lettuce and Weed Seedlings for Automated Weed Control." Weed Technology 22, no. 2 (April 2008): 378–84. http://dx.doi.org/10.1614/wt-07-104.1.

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Singh, Karan, K. N. Agrawal, and Ganesh C. Bora. "RETRACTED: Advanced techniques for Weed and crop identification for site specific Weed management." Biosystems Engineering 109, no. 1 (May 2011): 52–64. http://dx.doi.org/10.1016/j.biosystemseng.2011.02.002.

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Firmansyah, Erick, Teddy Suparyanto, Alam Ahmad Hidayat, and Bens Pardamean. "Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations." IOP Conference Series: Earth and Environmental Science 998, no. 1 (February 1, 2022): 012046. http://dx.doi.org/10.1088/1755-1315/998/1/012046.

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Abstract The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Tozer, K. N., C. M. Ferguson, and S. Glennie. "PestWeb a weed and pest information source for farmers." New Zealand Plant Protection 62 (August 1, 2009): 415. http://dx.doi.org/10.30843/nzpp.2009.62.4870.

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Weed and insect pests severely limit New Zealand pasture production and information on their management is often fragmented and difficult for farmers to access While there are a number of good pest and weed websites available many of these are commercial in focus or do not combine both identification and management options relevant to New Zealand farmers PestWeb (wwwagresearchconz/pestweb) is a website being created to assist farmers and agricultural professionals in decisionmaking regarding weed and pest identification biology impact and management A pilot site has been developed for identification and management of grass grub porina Californian thistle and barley grass and it is aimed to include an additional 2025 key New Zealand pasture weeds and pests which will be chosen in consultation with key farming industry and research personnel This site will provide independent information in an easily accessible and intuitive format to assist farmers in weed and pest management decisionmaking PestWeb development has involved collaboration between farmers of the South and West Otago Monitor Farm group farming consultants scientists and web designers This has ensured that the site is farmer friendly while providing independent peerreviewed information on the biology control and impact of key pests and weeds

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Asai, Motoaki. "Extension of early weed identification tool through novel picture books." Journal of Weed Science and Technology 63, no. 2 (2018): 33–36. http://dx.doi.org/10.3719/weed.63.33.

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Ambaw, Asfaw, Yohannes Ebabuye, and Yimer Abeje. "Qualitative and Quantitative Assessment of Common Weed Species in West Gondar Sesame Growing Area, Ethiopia." ABC Journal of Advanced Research 9, no. 2 (August 18, 2020): 53–62. http://dx.doi.org/10.18034/abcjar.v9i2.507.

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Weeds compete with crops grown for resource limits such as water, nutrients, and light. Weed infestation is one of the major factors limiting sesame production, as its seedlings grow slowly during the first few weeks making it a weak competitor at earlier crop growth stages. Weed induced sesame yield reductions of up to 65 percent, and a need for a crucial weed-free duration of up to 50 days following planting. Weed surveys are useful for determining the occurrence and relative importance of weed species in crop production systems. Weed survey on a farm basis is needed to establish efficient weed management and decision making mechanisms and to evaluate weed control measures. Therefore this survey was conducted to assess qualitative and quantitative weed species in sesame growing areas of west Gondar, Ethiopia. Weed survey was conducted in 2012 at Metema and 2013 main cropping season at Metema, Tach Armachiho, and Mirab Armachiho district. data on weed species type and count were taken along the diagonal with 50cm x 50cm quadrate at a 15-meter interval at three points. Visual identification, manuals, photographs, leaflets, and books were used for identifying weed species. They conducted interviews with farmers, development agents, and district experts working in the region. Weed species compositions were analyzed by frequency, abundance, dominance, and similarity index. In the districts, 31common weed species or groups of species from 17 families were recorded. Ipomea cordofana Br., Grass spp’, Commelina benghalensis L. Andropogon abyssinicus (Fresen) R.Br., Boerhaavia erecta L., Corchorus olitorius L.kudra, Corchorus trilocularis L., Leucas martinicensis (Jacq) ait.f. and Ipomea triloba are the most prevalent, abundant, and important weeds in sesame fields in north Gondar. As to this on-farm and practical training on identification and demonstration management options should be given on the low land of north Gondar farmers.

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Gonzales, Angelina. "SURVEY, IDENTIFICATION AND CONTROL OF WEEDS ASSOCIATED WITH MULBERRY (Morus spp.) IN THE PROVINCES OF AURORA, LA UNION, ILOCOS SUR AND ILOCOS NORTE, PHILIPPINES." International Journal of Research and Technology in Agriculture and Fisheries 1, no. 1 (December 30, 2021): 29–35. http://dx.doi.org/10.55687/ijrtaf.v1i1.2.

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Surveys, collections and identification of weeds were done in the sericulture projects of Sta. Maria, Ilocos Sur; Marcos, Ilocos Norte; Aurora, Aurora; and aat the SRDI, Bacnotan, La Union, in both wet and dry seasons. A total of 75 species of weeds were collected and identified. Survey, collection and identification of weeds in the sericulture projects of Sta Maria, Ilocos Sur;Marcos, Ilocos Norte; Ma. Aurora, Aurora; and at the Sericulture Research Development Institute in Bacnotan, La Union were done through quadrant sampling. Dominant weeds during the wet season in Sta Maria, Aurora, and Bacnotan were Digitaria sp., Fimbristylis littoralis, Ageratum conyzoides and Cyperus rotundus while during dry season, Diqitaria sp., Chloris sp., Centrosema pubescens and Dactyloctenium aeqyptium were the dominant weed species, respectively. On a per hectare basis, results showed mulching was found to be the most economical and beneficial weed control method. Total cost of weed control was P5,080.00 as against herbicide application with P7,224.00 and hand-weeding at P7,620.00.

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Mu, Ye, Ruilong Feng, Ruiwen Ni, Ji Li, Tianye Luo, Tonghe Liu, Xue Li, et al. "A Faster R-CNN-Based Model for the Identification of Weed Seedling." Agronomy 12, no. 11 (November 16, 2022): 2867. http://dx.doi.org/10.3390/agronomy12112867.

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The accurate and rapid acquisition of crop and weed information is an important prerequisite for automated weeding operations. This paper proposes the application of a network model based on Faster R-CNN for weed identification in images of cropping areas. The feature pyramid network (FPN) algorithm is integrated into the Faster R-CNN network to improve recognition accuracy. The Faster R-CNN deep learning network model is used to share convolution features, and the ResNeXt network is fused with FPN for feature extractions. Tests using >3000 images for training and >1000 images for testing demonstrate a recognition accuracy of >95%. The proposed method can effectively detect weeds in images with complex backgrounds taken in the field, thereby facilitating accurate automated weed control systems.

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Liu, Jian, and Bo Hu. "Field Weed Community Identification Algorithm with Euler Number." Journal of Physics: Conference Series 1852, no. 4 (April 1, 2021): 042067. http://dx.doi.org/10.1088/1742-6596/1852/4/042067.

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YANG, YANA, YANG LI, JIACHEN YANG, and JIABAO WEN. "Dissimilarity-based active learning for embedded weed identification." Turkish Journal of Agriculture and Forestry 46, no. 3 (January 1, 2022): 390–401. http://dx.doi.org/10.55730/1300-011x.3011.

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Nakayama, Yuichiro, Shinya Umemoto, and Hirofumi Yamaguchi. "Identification of Polyploid Groups in the Genus Echinochloa by Isozyme Analysis." Journal of Weed Science and Technology 44, no. 3 (1999): 205–17. http://dx.doi.org/10.3719/weed.44.205.

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Takahashi, M., M. Hasegawa, O. Kodama, and K. Kobayashi. "Identification of phytotoxic compounds in Tithonia diversifolia." Journal of Weed Science and Technology 46, Supplement (2001): 232–33. http://dx.doi.org/10.3719/weed.46.supplement_232.

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Chen, Zhengqiang, and Zhaomin Ma. "A Review: The Survey of the Effects of Light on Weed Recognition." MATEC Web of Conferences 228 (2018): 04008. http://dx.doi.org/10.1051/matecconf/201822804008.

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Weeding is one of the important tasks in agricultural field management. With the development of society and information technology, automatic weeding has become a developing trend. The automatic recognition of weeds based visual is the important step. In this paper, the effects of lighting on green identification and weed identification algorithms are summarized. In order to improve the accuracy and stability of the identification of crops and weeds, some further worth problems in the study are also put forward. This will help further research on automatic weeding.

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Oliver, Lawrence R. "A Decade of Weed Contests." Weed Technology 5, no. 2 (June 1991): 453–59. http://dx.doi.org/10.1017/s0890037x00028426.

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The first weed contest was held at the Eli Lilly Research Farm at Albany, GA, in late May 1980. From the original idea of David Teem of FL, a contest evolved that was sanctioned by the Southern Weed Science Society in 1981. In August 1981, the first North Central Collegiate Weed Science Contest was held at the Agri-Growth Research Farm, Hollandale, MN. An undergraduate division was established in 1982. The Northeast Collegiate Weed Science Contest began in 1983 at the Wye Research Center in Queenstown, MD, with both graduate and undergraduate formats. The North Central contest annually has the most participants followed by the Southern and Northeast contests. Representatives from 39 universities have competed in at least one contest. The three contests follow a similar format that includes four events: plant and seed identification of weed species, identification of herbicides from plant symptoms, calibration of herbicide application equipment, and crop/weed problems and recommendations. Preparation by universities has varied from minimal to establishing a class to prepare students for the contest. Regardless of the level of preparation, the weed contests have developed into one of the most anticipated and worthwhile experiences for students and university and industry personnel.

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Ghatrehsamani, Shirin, Gaurav Jha, Writuparna Dutta, Faezeh Molaei, Farshina Nazrul, Mathieu Fortin, Sangeeta Bansal, Udit Debangshi, and Jasmine Neupane. "Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review." Sustainability 15, no. 3 (January 18, 2023): 1843. http://dx.doi.org/10.3390/su15031843.

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The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.

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Mamora, Samuel Herbert. "Floristic Composition and Diversity of Weeds in Organic Rice Fields in Langkong, M’lang, Cotabato." Southeastern Philippines Journal of Research and Development 26, no. 1 (March 31, 2021): 35–48. http://dx.doi.org/10.53899/spjrd.v26i1.123.

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This study investigated the floristic composition and diversity of weeds in organic rice fields in Langkong, Mlang, Cotabato covering an area of 2.5 hectares within which 100 quadrats were randomly assigned. Identification of weeds showed thirteen species belonging to six families eight of which are annuals and five perennials comprising five broadleaf, three grass, and five sedge types. All weed species had <50% uniformity which may imply less competitiveness against rice or effective control by weed management practices. Fimbristylis littoralis and Cyperus difformis have the highest frequencies and the highest field uniformities and highest mean field densities indicating that these weeds are the most difficult to control. The weed density of fields in which the species occurred increased compared to densities from all fields for all weed species that may mean that site-specific or management-specific factors contribute to the survival of those species. Relative abundance values showed that Fimbristylis littoralis and Cyperus difformis are the two most dominant weed species. Weed species diversity is medium and equivalent to 5 equally abundant species.

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Etienne, Aaron, Aanis Ahmad, Varun Aggarwal, and Dharmendra Saraswat. "Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery." Remote Sensing 13, no. 24 (December 20, 2021): 5182. http://dx.doi.org/10.3390/rs13245182.

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Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures for targeted herbicide application within a field. However, a major challenge to developing a weed detection system is the requirement for a properly annotated database to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of 374 red, green, and blue (RGB) color images organized into monocot and dicot weed classes. The images were acquired from corn and soybean research plots located in north-central Indiana using an unmanned aerial system (UAS) flown at 30 and 10 m heights above ground level (AGL). A total of 25,560 individual weed instances were manually annotated. The annotated database consisted of four different subsets (Training Image Sets 1–4) to train the You Only Look Once version 3 (YOLOv3) deep learning model for five separate experiments. The best results were observed with Training Image Set 4, consisting of images acquired at 10 m AGL. For monocot and dicot weeds, respectively, an average precision (AP) score of 91.48 % and 86.13% was observed at a 25% IoU threshold (AP @ T = 0.25), as well as 63.37% and 45.13% at a 50% IoU threshold (AP @ T = 0.5). This research has demonstrated a need to develop large, annotated weed databases to evaluate deep learning models for weed identification under field conditions. It also affirms the findings of other limited research studies utilizing object detection for weed identification under field conditions.

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Castro, Alan Carlos de Oliveira, Alaerson Maia Geraldine, Renata Pereira Marques, Pedro Jacob Christoffoleti, Ana Paula Lopes Dias, Matheus Vinicius Abadia Ventura, Tavvs Micael Alves, Bruno Henrique Tondato Arantes, Claiton Gomes dos Santos, and Eugenio Miranda Sperandio. "Identification of Weed Species in Commercial Soybean Areas by High-Resolution Drone Images." Journal of Agricultural Science 14, no. 3 (February 15, 2022): 123. http://dx.doi.org/10.5539/jas.v14n3p123.

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Multispectral sensors onboard remotely piloted aircraft systems (RPAs) can be used for mapping and identifying weed species and preventing crop yield losses. The objective of this study was to identify and quantify weed species in soybean using high-resolution images obtained by an RPA. Soybean fields were photographed 33 times every 100 ha. Weed flora in 384 sampling areas was surveyed by aerial imaging in approximately 60.000 ha. Results on analysis of the community structure of the observed a total of 16 plant families and 52 species. Species from Asteraceae and Poaceae were the most numerous. Results of principal component analysis showed that the percentage of infestation and the number of species were positively correlated to the first component. The areas with the highest percentage of infestation had the highest diversity of species. However, the percentage of infestation and the number of species observed were not correlated with the area size. The survey of weeds by aerial imagery was efficient for identifying, quantifying, and mapping weeds in commercial agricultural areas and can be used in other studies and for the purposes of management in commercial areas.

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Zhang, Xinle, Jian Cui, Huanjun Liu, Yongqi Han, Hongfu Ai, Chang Dong, Jiaru Zhang, and Yunxiang Chu. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm." Agriculture 13, no. 1 (January 10, 2023): 175. http://dx.doi.org/10.3390/agriculture13010175.

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Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low. Therefore, a weed identification method is proposed based on the optimized Faster R-CNN algorithm for the soybean seedling. Three types of weed datasets, including soybean, with a total of 9816 photos were constructed, and cell phone photo data were used for training and recognition. Firstly, by comparing the classification effects of ResNet50, VGG16, and VGG19, VGG19 was identified as the best backbone feature extraction network for model training. Secondly, an attention mechanism was embedded after the pooling layer in the second half of VGG19 to form the VGG19-CBAM structure, which solved the problem of low attention to the attention target during model training using the trained Faster R-CNN algorithm to identify soybean and weeds in the field under the natural environment and compared with two classical target detection algorithms, SSD and Yolov4. The experimental results show that the Faster R-CNN algorithm using VGG19-CBAM as the backbone feature extraction network can effectively identify soybeans and weeds in complex backgrounds. The average recognition speed for a single image is 336 ms, and the average recognition accuracy is 99.16%, which is 5.61% higher than before optimization, 2.24% higher than the SSD algorithm, and 1.24% higher than the Yolov4 algorithm. Therefore, this paper’s optimized target detection model is advantageous and can provide a scientific method for accurate identification and monitoring of grass damage.

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Jabir, Brahim, Loubna Rabhi, and Noureddine Falih. "RNN- and CNN-based weed detection for crop improvement: An overview." Foods and Raw Materials 9, no. 2 (November 9, 2021): 387–96. http://dx.doi.org/10.21603/2308-4057-2021-2-387-396.

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Introduction. Deep learning is a modern technique for image processing and data analysis with promising results and great potential. Successfully applied in various fields, it has recently entered the field of agriculture to address such agricultural problems as disease identification, fruit/plant classification, fruit counting, pest identification, and weed detection. The latter was the subject of our work. Weeds are harmful plants that grow in crops, competing for things like sunlight and water and causing crop yield losses. Traditional data processing techniques have several limitations and consume a lot of time. Therefore, we aimed to take inventory of deep learning networks used in agriculture and conduct experiments to reveal the most efficient ones for weed control. Study objects and methods. We used new advanced algorithms based on deep learning to process data in real time with high precision and efficiency. These algorithms were trained on a dataset containing real images of weeds taken from Moroccan fields. Results and discussion. The analysis of deep learning methods and algorithms trained to detect weeds showed that the Convolutional Neural Network is the most widely used in agriculture and the most efficient in weed detection compared to others, such as the Recurrent Neural Network. Conclusion. Since the Convolutional Neural Network demonstrated excellent accuracy in weed detection, we adopted it in building a smart system for detecting weeds and spraying them in place.

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Shah, Tavseef Mairaj, Durga Prasad Babu Nasika, and Ralf Otterpohl. "Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification." Agriculture 11, no. 3 (March 8, 2021): 222. http://dx.doi.org/10.3390/agriculture11030222.

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Farming systems form the backbone of the world food system. The food system, in turn, is a critical component in sustainable development, with direct linkages to the social, economic, and ecological systems. Weeds are one of the major factors responsible for the crop yield gap in the different regions of the world. In this work, a plant and weed identifier tool was conceptualized, developed, and trained based on artificial deep neural networks to be used for the purpose of weeding the inter-row space in crop fields. A high-level design of the weeding robot is conceptualized and proposed as a solution to the problem of weed infestation in farming systems. The implementation process includes data collection, data pre-processing, training and optimizing a neural network model. A selective pre-trained neural network model was considered for implementing the task of plant and weed identification. The faster R-CNN (Region based Convolution Neural Network) method achieved an overall mean Average Precision (mAP) of around 31% while considering the learning rate hyperparameter of 0.0002. In the plant and weed prediction tests, prediction values in the range of 88–98% were observed in comparison to the ground truth. While as on a completely unknown dataset of plants and weeds, predictions were observed in the range of 67–95% for plants, and 84% to 99% in the case of weeds. In addition to that, a simple yet unique stem estimation technique for the identified weeds based on bounding box localization of the object inside the image frame is proposed.

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James, T. K., C. A. Dowsett, and M. R. Trolove. "Identification and enumeration of weed seeds in chopped maize being transported for silage." New Zealand Plant Protection 68 (January 8, 2015): 118–23. http://dx.doi.org/10.30843/nzpp.2015.68.5879.

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Maize silage is an important dairy feed supplement and is often transported many kilometres from where it is grown to where it is ensiled Debris has been observed to blow from the transporting trucks as they travel along the road raising concerns about the spread of weeds In this study trucks carrying freshly chopped maize were intercepted as they departed the field and samples from inside and outside the loaded crate were collected and analysed for weed seeds Seeds from 15 weed species were found in numbers up to 1300 seeds/kg of freshly chopped maize Generally more seed was found in trucks collecting from the headland area compared with the main crop area and additionally in the first year there was more debris on the outside of the headland trucks Some mitigating practices to reduce the potential for dispersal of weed seeds in chopped maize are discussed

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Kamitani, T., Y. Nakayama, and H. Yamaguchi. "Identification of Sparganium species based on cpDNA sequences." Journal of Weed Science and Technology 50, Supplement (2005): 14–15. http://dx.doi.org/10.3719/weed.50.supplement_14.

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Pando, Francisco, Ignacio Heredia, Carlos Aedo Pérez, Mauricio Velayos Rodríguez, Lara Lloret Iglesias, and Joel Calvo. "Deep learning for weed identification based on seed images." Biodiversity Information Science and Standards 2 (May 21, 2018): e25749. http://dx.doi.org/10.3897/biss.2.25749.

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Reliable plant species identification from seeds is intrinsically difficult due to the scarcity of features and because it requires specialized expertise that is becoming increasingly rarer, as the number of field plant taxonomists is diminishing (Bacher 2012, Haas and Häuser 2005). On the other hand, seed identification is relevant in some science domains such as plant community ecology, archaeology, paleoclimatology. Besides, economic activities such as agriculture, require seed identification to assess weed species contained in the "soil seed banks" (Colbach 2014) to enable targeted treatments before they become a problem. In this work, we explore and evaluate several approaches by using different training image sets with various requisites and assessing their performance with test datasets of different sources. The core training dataset is provided by the Anthos project (Castroviejo et al. 2017) as a subset of its image collection. It consists of nearly a 1000 images of seeds identified by experts. As identification algorithm, we will use state-of-the-art convolutional neural networks for image classification (He et al. 2016). The framework is fully written in Python using the TensorFlow (Abadi et al. 2016) module for deep learning.

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Zhang, Yangkai, Mengke Wang, Danlei Zhao, Chunye Liu, and Zhengguang Liu. "Early weed identification based on deep learning: A review." Smart Agricultural Technology 3 (February 2023): 100123. http://dx.doi.org/10.1016/j.atech.2022.100123.

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Pomar, J., and I. Hidalgo. "An intelligent multimedia system for identification of weed seedlings." Computers and Electronics in Agriculture 19, no. 3 (March 1998): 249–64. http://dx.doi.org/10.1016/s0168-1699(97)00045-8.

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Trichard, Aude, Audrey Alignier, Bruno Chauvel, and Sandrine Petit. "Identification of weed community traits response to conservation agriculture." Agriculture, Ecosystems & Environment 179 (October 2013): 179–86. http://dx.doi.org/10.1016/j.agee.2013.08.012.

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Dholakia, Bakul H. "Industrial Sickness in India: Weed for Comprehensive Identification Criteria." Vikalpa: The Journal for Decision Makers 14, no. 2 (April 1989): 19–24. http://dx.doi.org/10.1177/0256090919890204.

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Prevention is better than cure—this adage is particularly significant in the context of growing industrial sickness in the country. Fundamental to the criteria used by financial institutions and government agencies to identify sickness is the recurrence of cash loss. Dholakia argues that use of various criteria based on the cash loss syndrome delays identification of sickness and results in a high proportion of terminally sick units. According to Dholakia, what is needed is a comprehensive set of empirically tested criteria which would serve as an early warning system. Abnormal fluctuations in a firm's relative position within the industry to which it belongs should be explicitly used to determine sickness at the incipient stage. This is likely to help prevent industrial sickness. However, this would require restructuring of existing systems and procedures adopted by the financial institutions.

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Lindow, Steven E., and Gary L. Andersen. "Microcomputer Measurements of Pathogen Injury to Weeds." Weed Science 34, S1 (1986): 38–42. http://dx.doi.org/10.1017/s0043174500068375.

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The impact of chemical or biological herbicides in weed control may or may not be absolute. The traditional goal in chemical herbicide evaluation has been the identification of materials and application rates resulting in absolute (100%) kill of susceptible weeds. However, many insects or plant pathogens affecting weeds do not kill their weed hosts but decrease their growth and or reproduction in the field. Obligate plant pathogens dependent upon their weed hosts for their own survival derive no evolutionary advantage from killing their weed hosts and seldom do so under natural conditions. Under field conditions where weeds are subjected to stresses imposed by competition with crop and/or other wild plants, these biological agents can decrease the impact of a weed on crop or wildland productivity to tolerable economic levels. The recognition that the impact of chemical and/or biological herbicides on weeds need not be absolute to be effective has been accompanied by a search for an accurate, efficient, and economical method to measure quantitative changes in weed growth or health. While much of this report will concentrate on measuring impact of plant pathogens on weeds or other plants, all comments and techniques will apply also to other stresses such as herbicide injury.

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Journal articles on the topic 'Weed identification'

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