Classification of Weeds of Paddy Fields using Deep Learning

Main Article Content

Radhika Kamath
Mamatha Balachandra
Amodini Vardhan
Ujjwal Maheshwari


Weed management is one of the important tasks in agriculture. Weeds in rice fields are usually managed using three ways - chemical herbicides, mechanical weeders, and manual weeding. Manual weeding becomes a problem when there is a shortage of agricultural laborers. Mechanical weeders are not suitable for direct-seeded rice fields. Chemical herbicides are not advisable especially when farmers do not know about site-specific weed management. Site-specific weed management is using the right herbicide in the right amount. Therefore, this paper investigates computer vision-based deep learning techniques with transfer learning classifying three types of weeds in paddy fields, namely sedges, grasses, and broadleaved weeds so that the right herbicide is recommended to the farmers. This would reduce the broadcast application and the overuse of the herbicides, thereby limiting the negative impact of the chemical herbicides on the environment. This research work shows promising results with an accuracy around 90% and thus encourages the development of digital agriculture.

Article Details

How to Cite
R. Kamath, M. Balachandra, A. . Vardhan, and U. Maheshwari, “Classification of Weeds of Paddy Fields using Deep Learning”, ECTI-CIT Transactions, vol. 16, no. 4, pp. 365–377, Sep. 2022.
Research Article


Mare Srbinovska, Cvetan Gavrovski, Vladimir Dimcev, Aleksandra Krkoleva, and Vesna Borozan. Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of cleaner production, 88:297– 307, 2015.

Barun Basnet and Junho Bang. The state-of-the-art of knowledge-intensive agriculture: A review on applied sensing systems and data analytics. Journal of Sensors, 2018, 2018.

Stefanos A Nikolidakis, Dionisis Kandris, Dimitrios D Vergados, and Christos Douligeris. Energy efficient automated control of irrigation in agriculture by using wireless sensor networks. Computers and Electronics in Agriculture, 113:154–163, 2015.

Tamoghna Ojha, Sudip Misra, and Narendra Singh Raghuwanshi. Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and electronics in agriculture, 118:66–84, 2015.

Zhao Liqiang, Yin Shouyi, Liu Leibo, Zhang Zhen, and Wei Shaojun. A crop monitoring system based on wireless sensor network. Procedia Environmental Sciences, 11:558–565, 2011.

W Chung and C Chen. Design of wireless monitoring and image capturing system based on zigbee for agricultural greenhouse. In The 8th Asian Federation for Information Technology in Agriculture (AFITA2012) and World Conference on Computers in Agriculture (WCCA 2012), 2012.

P Tirelli, NA Borghese, F Pedersini, G Galassi, and R Oberti. Automatic monitoring of pest insects traps by zigbee-based wireless networking of image sensors. In 2011 IEEE International Instrumentation and Measurement Technology Conference, pages 1–5. IEEE, 2011.

G Nisha and J Megala. Wireless sensor network-based automated irrigation and crop field monitoring system. In 2014 Sixth international conference on advanced computing (IcoAC), pages 189–194. IEEE, 2014.

Hongkun Tian, Tianhai Wang, Yadong Liu, Xi Qiao, and Yanzhou Li. Computer vision technology in agricultural automation—a review. Information Processing in Agriculture, 7(1):1–19, 2020.

Chung-Liang Chang and Kuan-Ming Lin. Smart agricultural machine with a computer vision-based weeding and variable-rate irrigation scheme. Robotics, 7(3):38, 2018.

Fabrizio Balducci, Donato Impedovo, and Giuseppe Pirlo. Machine learning applications on agricultural datasets for smart farm enhancement. Machines, 6(3):38, 2018.

Faisal Ahmed, Hawlader Abdullah Al-Mamun, ASM Hossain Bari, Emam Hossain, and Paul Kwan. Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40:98–104, 2012.

Zhichen Li, Qiu An, and Changying Ji. Classification of weed species using artificial neural network based on color leaf texture feature. In International Conference on Computer and Computing Technologies in Agriculture, pages 1217– 1225. Springer, 2008.

Martin Weis and Markus S¨okefeld. Detection and identification of weeds. Precision crop protection-the challenge and use of heterogeneity, pages 119–134, 2010.

Thomas M Giselsson, Henrik S Midtiby, Rasmus N Jørgensen, et al. Seedling discrimination using shape features derived from a distance transform. In Information Technology, Automation and Precision Farming. International Conference of Agricultural Engineering CIGR AgEng 2012: Agriculture and Engineering for a Healthier Life, Valencia, Spain, 8-12 July 2012. CIGR-Eur AgEng, 2012.

Sebastian Haug and J¨orn Ostermann. A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. In European Conference on Computer Vision, pages 105–116. Springer, 2014.

Pedro Javier Herrera, Jos´e Dorado, and A´ngela Ribeiro. A novel approach for weed type classification based on shape descriptors and a fuzzy decision-making method. Sensors, 14(8):15304– 15324, 2014.

Ming Kuei Hu. Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2):179–187, 1962.

Adel Bakhshipour and Abdolabbas Jafari. Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture, 145:153–160, 2018.

Philipp Lottes, Jens Behley, Nived Chebrolu, Andres Milioto, and Cyrill Stachniss. Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming. Journal of Field Robotics, 37(1):20–34, 2020.

George E Meyer. Machine vision identification of plants. IntechOpen, 2011.

Manuel Alejandro Molina-Villa, Leonardo Enrique Solaque-Guzm´an, et al. Machine vision system for weed detection using image filtering in vegetables crops. Revista Facultad de Ingenier´ıa Universidad de Antioquia, (80):124–130, 2016.

JC Negrete. Artificial vision in mexican agriculture for identification of diseases, pests and invasive plants. Journal of Advanced Plant Science, 1:303, 2018.

DC Slaughter, DK Giles, and D Downey. Autonomous robotic weed control systems: A review. Computers and electronics in agriculture, 61(1):63–78, 2008.

Sun Hong, Li Minzan, and Zhang Qin. Detection system of smart sprayers: Status, challenges, and perspectives. International Journal of Agricultural and Biological Engineering, 5(3):10–23, 2012.

Andreas Kamilaris and Francesc X Prenafeta Boldu´. Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147:70–90, 2018.

Ju¨rgen Schmidhuber. Deep learning in neural networks: An overview. Neural networks, 61:85– 117, 2015.

Yoshua Bengio, Yann LeCun, Craig Nohl, and Chris Burges. Lerec: A nn/hmm hybrid for online handwriting recognition. Neural computation, 7(6):1289–1303, 1995.

Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. A survey on deep transfer learning. In International conference on artificial neural networks, pages 270–279. Springer, 2018.

Jaechang Nam, Wei Fu, Sunghun Kim, Tim Menzies, and Lin Tan. Heterogeneous defect prediction. IEEE Transactions on Software Engineering, 44(9):874–896, 2017.

Chang Wang and Sridhar Mahadevan. Heterogeneous domain adaptation using manifold alignment. In IJCAI proceedings-international joint conference on artificial intelligence, volume 22, page 1541, 2011.

Diane J Cook, Narayanan C Krishnan, and Parisa Rashidi. Activity discovery and activity recognition: A new partnership. IEEE transactions on cybernetics, 43(3):820–828, 2013.

Yogita Gharde, PK Singh, RP Dubey, and PK Gupta. Assessment of yield and economic losses in agriculture due to weeds in India. Crop Protection, 107:12–18, 2018.

Lise Nistrup Jørgensen, Egon Noe, Anne-Mette Langvad, JE Jensen, Jens Erik Ørum, and P Rydahl. Decision support systems: barriers and farmers’ need for support. EPPO bulletin, 37(2):374–377, 2007.

C´ordova-Cruzatty Andrea, Barreno Barreno Mauricio Daniel, and J´acome Barrionuevo Jos´e Misael. Precise weed and maize classification through convolutional neuronal networks. In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), pages 1–6. IEEE, 2017.

R Kingsy Grace et al. Crop and weed classification using deep learning. Turkish Journal of Computer and Mathematics Education (TUR- COMAT), 12(7):935–938, 2021.

Prince Sharma. Crops and weeds classification using convolutional neural networks via optimization of transfer learning parameters. International Journal of Engineering and Advanced Technology (IJEAT) ISSN, pages 2249–8958, 2019.

Thomas Mosgaard Giselsson, Rasmus Nyholm Jørgensen, Peter Kryger Jensen, Mads Dyrmann, and Henrik Skov Midtiby. A public image database for benchmark of plant seedling classification algorithms. arXiv preprint arXiv:1711.05458, 2017.

Jiang Haichen, Chang Qingrui, and Liu Zheng Guang. Weeds and crops classification using deep convolutional neural network. In 2020 the 3rd International Conference on Control and Computer Vision, pages 40–44, 2020.

Jialin Yu, Arnold W Schumann, Zhe Cao, Shaun M Sharpe, and Nathan S Boyd. Weed detection in perennial ryegrass with deep learning convolutional neural network. Frontiers in plant science, 10, 2019.

Xu Ma, Xiangwu Deng, Long Qi, Yu Jiang, Hongwei Li, Yuwei Wang, and Xupo Xing. Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields. PloS one, 14(4):e0215676, 2019.

Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentationIEEE transactions on pattern analysis machine intelligence, 39(12):2481–2495, 2017.

Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440, 2015.

Inkyu Sa, Zetao Chen, Marija Popovi´c, Raghav Khanna, Frank Liebisch, Juan Nieto, and Roland Siegwart. weednet: Dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robotics and Automation Letters, 3(1):588–595, 2017.

Andres Milioto, Philipp Lottes, and Cyrill Stachniss. Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 2229–2235. IEEE, 2018.

Muhammad Hamza Asad and Abdul Bais. Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture, 2019.

Mulham Fawakherji, Ali Youssef, Domenico Bloisi, Alberto Pretto, and Daniele Nardi. Crop and weeds classification for precision agriculture using context-independent pixel-wise segmentation. In 2019 Third IEEE International ConferenceRobotic Computing (IRC), pages 146–152. IEEE, 2019.

Lukasz Chechlinski, Barbara Siemitkowska, and Michal Majewski. A system for weeds and crops identification—reaching over 10 fps on raspberry pi with the usage of mobilenets, densenet and custom modifications. Sensors, 19(17):3787, 2019.

Bo Liu and Ryan Bruch. Weed detection for selective spraying: a review. Current Robotics Reports, 1(1):19–26, 2020.

Joseph Redmon and Ali Farhadi. Yolo9000: bet- ter, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7263–7271, 2017.

PK Mukherjee, Anindya Sarkar, and Swa- pan Kumar Maity. Critical period of crop- weed competition in transplanted and wet seeded kharif rice (oryza sativa l.) under terai conditions. Indian Journal of Weed Science, 40(3and4):147–152, 2008.

Gulshan Mahajan, Bhagirath S Chauhan, and Vivek Kumar. Integrated weed management in rice. In Recent advances in weed management, pages 125–153. Springer, 2014.

Radhika Kamath, Mamatha Balachandra, and Srikanth Prabhu. Raspberry pi as visual sensor nodes in precision agriculture: A study. IEEE Access, 7:45110–45122, 2019.

Radhika Kamath, Mamatha Balachandra, and Srikanth Prabhu. Paddy crop and weed discrimination: A multiple classifier system approaches. International Journal of Agronomy, 2020, 2020.

Radhika Kamath, Mamatha Balachanra, and Srikanth Prabhu. Paddy crop and weed classification using color features for computer vision-based precision agriculture. International Journal of Engineering and Technology (UAE), 7(4):2909–2916, 2018.

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25:1097–1105, 2012.

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.

Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4):427–437, 2009.

ASM Mahmudul Hasan, Ferdous Sohel, Dean Diepeveen, Hamid Laga, and Michael GK Jones. A survey of deep learning techniques for weed detection from images. Computers and Electronics in Agriculture, 184:106067, 2021.