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This groundbreaking research introduces an AI-based approach for revolutionizing weed management in legume farmland, addressing the limitations of traditional methods and introducing a new era of cost-effective and precise weed detection and removal. Traditional methods of removing weeds from farmland involving machinery or chemicals often resulted in high costs and imprecise outcomes. To address these challenges, an advanced image recognition algorithm was proposed, which harnessed smart machines to minimize costs and environmental risks. By utilizing computer vision technology, weeds were accurately identified and targeted for removal. A machine learning model was trained using relevant datasets to enable precise weed management. The AI-powered robot, equipped with advanced image recognition algorithms, demonstrated exceptional accuracy and speed, performing weed removal and decomposition 1.2 times faster than traditional manual labour. This breakthrough in weed management technology offers farmers a means to optimize crop yields, enhance food production, and minimize the environmental impact associated with chemical herbicides. A prototype of the robot was fabricated and evaluated in real-world farming conditions. Field tests were conducted on a bean farm and it’s demonstrated the robot's exceptional accuracy, with only a 2% deviation from the actual weed quantity. This research showcased the potential of AI-based weed management systems in legume farming, offering cost-effective and precise weed detection and removal. This research sets a precedent for the integration of AI in modern agriculture, driving the industry toward a more environmentally conscious and economically viable future. The AI-based weed management system empowers farmers, ensuring bountiful harvests, increased profitability, and a greener, more sustainable tomorrow while attention should be given to manufacturing this model for industrial and or commercial applications.
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 Peteinatos, G.G., Reichel, P., Karouta, J. & Andújar, D.R. (2020). Weed Identification in Maize, Sunflower, and Potatoes with The Aid of Convolutional Neural Networks. Remote Sens. 12(24), 1–22. Doi: 10.3390/rs12244185.
 Patnaik, A. & Narayanamoorthi, R. (2015). Weed Removal in Cultivated Field by Autonomous Robot Using LABVIEW. ICIIECS 2015 - 2015 IEEE Int. Conf. Innov. Information, Embed. Commun. Syst. Doi: 10.1109/ICIIECS.2015.7193168.
 Islam, N., Wibowo, S., Rashid, M.M., Cheng-Yuan, X., Morhed, A., Wasimi, S.A., Moore, S. & Rahman, S.M. (2021). Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm. Agric.11(5). Doi: 10.3390/agriculture11050387.
 Slaughter, D.C., Giles, D.K. & Downey, D. (2008). Autonomous Robotic Weed Control Systems: A Review. Comput. Electron. Agric. 61(1), 63–78. Doi: 10.1016/j.compag.2007.05.008.
 Tian, H., Wang, T., Liu, Y., Qiao, X. & Li, Y. (2020). Computer Vision Technology in Agricultural Automation —A Review. Inf. Process. Agric. 7(1), 1–19. Doi: 10.1016/j.inpa.2019.09.006.
 Esposito, M., Crimaldi, M., Cirillo, V., Sarghini, F. & Maggio, A. (2021). Drone and Sensor Technology for Sustainable Weed Management: A Review. Chem. Biol. Technol. Agric. 8(1), 1–11. Doi: 10.1186/s40538-021-00217-8.
 Burgos-Artizzu, X.P., Ribeiro, A., Guijarro, M. & Pajares, G. (2011). Real-Time Image Processing for Crop/Weed Discrimination in Maize Fields. Comput. Electron. Agric. 75(2), 337–346. Doi: 10.1016/j.compag.2010.12.011.
 Nidhi, G., Bharat, G., Kalpdrum, P. & Chakresh, K.J. (2022). Applications of Artificial Intelligence Based Technologies in Weed and Pest Detection. Journal of Computer Sciences. 18(6), 520.529. DOI: 10.3844/jcssp.2022.520-529.
 Cheng, B. & Matson, E.T. (2015). A feature-based machine learning agent for automatic rice and weed discrimination. Lect. Notes Artif. Intell. Subseries Lect. Notes Comput. Sci. 9119, 517–527. Doi: 10.1007/978-3-319-19324-3_46.
 Steward, B., Gai, J. & Tang, L. (2019). The Use of Agricultural Robots in Weed Management and Control. Agric. Robot. weed Manag. Control. 161–186. Doi: 10.19103/as.2019.0056.13.
 Baerveldt, A. & Åstrand, B. (2002). An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control. Autonomous Robots, 13, 21–35. https://doi.org/10.1023/A:1015674004201
 Wu, Z., Chen, Y., Zhao, B., Kang, X. & Ding, Y. (2021). Review of Weed Detection Methods Based on Computer Vision. Sensors. 21(11), 1–23. Doi: 10.3390/s21113647.
 Ghatrehsamani, S., Jha, G., Dutta, W., Molaei, F., Nazrul, F., Fortin, M., Bansal, S., Debangshi, U. & Neupane, J. (2023). Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review. Sustainability. 15(0), 1843. https://doi.org/10.3390/su15031843.
 Zhang, Q., Shaojie, M., Chen, E. & Li, B. (2017). A Visual Navigation Algorithm for Paddy Field Weeding Robot Based on Image Understanding. Comput. Electron. Agric. 143, 66–78. Doi: 10.1016/j.compag.2017.09.008.
 Chauhan, A. & Joshi, P.C. (2010). Composting of Some Dangerous and Toxic Weeds Using Eisenia foetida. Journal of America Science. 6 (3), 1-6.
 Cooperband, L.R. (2000). Composting: Art and Science of Organic Waste Conversion to a Valuable Soil Resource. Compost. Art Sci. Org. Waste Convers. to a Valuab. Soil Resour. Compost. 31(5), 283–289. Doi: 10.1309/w286-lqf1-r2m2-1wnt.
 Liu, S., Jin, Y., Ruan, Z., Ma, Z., Gao, R. & Su, Z. (2022). Real-Time Detection of Seedling Maize Weeds in Sustainable Agriculture. Sustain. 14(22). Doi: 10.3390/su142215088.
 Veeragandham, S. & Santhi, H. (2021). A Detailed Review on Challenges and Imperatives of Various CNN Algorithms in Weed Detection. Proc. Int. Conf. Artif. Intell. Smart Syst. ICAIS. 1068–1073. Doi: 10.1109/ICAIS50930.2021.9395986.
 Su, W.H. (2020). Crop Plant Signaling for Real-Time Plant Identification in Smart Farm: A Systematic Review and New Concept in Artificial Intelligence for Automated Weed Control. Artif. Intell. Agric. 4(0), 262–271. Doi: 10.1016/j.aiia.2020.11.001.
 Olaniyi, O.M., Daniya, E., Abdullahi, I.M., Bala, J.A. & Olanrewaju, A.E. (2009). Developing Intelligent Weed Computer Vision System for Low Land Rice Precision Farming. ICAAT 2009 Proceedings. 99 111. http://repository.futminna.edu.ng:8080/jspui/bitstream/123456789/18800/1/paper.pdf
 Gao, J., Nuyttens, D., Lootens, P., He, Y. & Pieters, J.G. (2018). Recognizing Weeds in a Maize Crop Using a Random Forest Machine-Learning Algorithm and Near-Infrared Snapshot Mosaic Hyperspectral Imagery. Biosyst. Eng. 170(0), 39–50. Doi: 10.1016/j.biosystemseng.2018.03.006.
 Arinola, B.A. & Michael, T.F. (2022). Development of a Semi-Automatic Hand-Pushed Weeder. ABUAD Journal of Engineering Research and Development (AJERD). 5(1), 134-146.
 Dankhara, F., Patel, K. & Doshi, N. (2019). Analysis of Robust Weed Detection Techniques Based on the Internet of Things (IoT). Procedia Comput. Sci. 160, 696–701. Doi: 10.1016/j.procs.2019.11.025.
 Rahman, A., Lu, Y. & Wang, H. (2023).Performance Evaluation of Deep Learning Object Detectors for Weed Detection for Cotton, Smart Agricultural Technology. 3(1), 100 -126.
 Kelly, B., Padayachee, X. J. & Bright, G. (2019). Quasi-serial Manipulator for Advanced Manufacturing Systems. Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications. Doi: 10.5220/0007839003000305.