Implementing Innovative Weed Detection Techniques for Environmental Sustainability
J. Environ. Nanotechnol., Volume 13, No 4 (2024) pp. 287-294
Abstract
Agriculture, supporting over half of India's population, grapples with the challenge of weed control. Current methods applied in plantation crops lack efficiency and pose environmental and health risks. This paper advocates a paradigm shift, emphasizing the critical need for effective weed detection using cluttered unmanned aerial vehicle (UAV) images. The research methodology integrates image processing, Mask R-Convolutional Neural Networks (R-CNN), and Internet of Things (IoT). A dataset of 200 UAV images was subjected to a thorough preprocessing. In the initial phase, weeds and crops were identified with precision employing an UAV-tailored Mask R-CNN with instance segmentation. This was found to surpass traditional methods in terms of communication between the model and the agricultural environment. For timely decision-making, real-time data were collected using IoT. Average Precision (AP) values reveal high accuracy, notably 89.1% for weeds, 88.9% for crops, and an overall precision of 89.4%. The Mask R-CNN network segments and classifies images, marking weed zones communicated to farmers via Raspberry Pi with a GSM module, enabling real-time alerts and informed decision-making for efficient weed control. This holistic approach, providing object classifications, detailed bounding boxes, and masks, addresses weed control challenges, highlighting the transformative potential of advanced technologies in agriculture.
Full Text
Reference
Abouziena, H. F. and Haggag, W. M., Weed control in clean agriculture: a review, Planta Daninha, 34(2), 377-392 (2016).
http://dx.doi.org/10.1590/S0100-83582016340200019
Bakhshipour, A. and Jafari, A., Evaluation of support vector machine and artificial neural networks in weed detection using shape features, Comput. Electron. Agric., 145, 153-160 (2018).
http://dx.doi.org/10.1016/j.compag.2017.12.032
Burgos, A. X. P., Ribeiro, A., Guijarro, M. and Pajares, G., Real-time image processing for crop/weed discrimination in maize fields, Comput. Electron. Agric., 75(2), 337-346 (2010).
https://doi.org/10.1016/j.compag.2010.12.011
Chouhan, S. S., Kaul, A., Singh, U. P. and Science, M. I. T., A database of leaf images: Practice towards plant conservation with plant pathology, Mendeley Data, V4, (2020).
https://doi.org/10.17632/hb74ynkjcn.4
Ferreira, D, S., Freitas, A. D. M., Da, S. G. G., Pistori, H. and Folhes, M. T., Weed detection in soybean crops using ConvNets, Comput. Electron. Agric., 143, 314-324 (2017).
https://doi.org/10.1016/j.compag.2017.10.027
Gatys, L. A., Ecker, A. S. and Bethge, M., A neural algorithm of artistic style, arXiv:1508.06576, (2015).
https://doi.org/10.48550/arXiv.1508.06576
Ghazali, K. H., Mustafa, M. M. and Hussain, A., Machine vision system for automatic weeding strategy in oil palm plantation using image filtering technique, InTechOpen., (2008).
Giselsson, T., Dyrmann, M., Jørgensen, R., Jensen, P. and Midtiby, H., A public image database for benchmark of plant seedling classification algorithms, arXiv:1711.05458 (2017).
https://doi.org/10.48550/arXiv.1711.05458
He, K., Gkioxari, G., Dollár, P. and Girshick, R., Mask R-CNN, 2017 IEEE International Conference on Computer Vision, 2961-2969 (2017).
https://doi.org/10.1109/ICCV.2017.322
Huang, M. L. and Chang, Y. H., Dataset of tomato leaves, Mendeley Data, V1, (2020).
https://doi.org/10.17632/ngdgg79rzb.1
Ishak, A. J., Mokri, S. S., Mustafa, M. M. and Hussain, A., Weed detection utilizing quadratic polynomial and ROI techniques, Proc. 5th Student Conf. Res. and Dev., (2007).
http://dx.doi.org/10.1109/SCORED.2007.4451360
Jayabal, R., Optimization and Impact of Modified Operating Parameters of a Diesel Engine Emissions Characteristic Utilizing Waste Fat Biodiesel/Di-Tert-Butyl Peroxide Blend, Process Saf. Environ. Prot., 186, 694–705 (2024).
https://doi.org/10.1016/j.psep.2024.04.019
Kannan, R., Ramalingam, S., Sampath, S., Nedunchezhiyan, M., Dillikannan, D. and Jayabal, R., Optimization and Synthesis Process of Biodiesel Production from Coconut Oil Using Central Composite Rotatable Design of Response Surface Methodology, Proc. Inst. Mech. Eng., Part E: J. Process Mech. Eng., (2024).
https://doi.org/10.1177/09544089241230251
Kargar, A. H. B. and Shirzadifar, A. M., Automatic weed detection system and smart herbicide sprayer robot for corn fields, 2013 First RSI/ISM International Conference on Robotics and Mechatronics, 468-473 (2013).
https://doi.org/10.1109/ICRoM.2013.6510152
Krishnamoorthi, T., Sudalaimuthu, G., Dillikannan, D. and Jayabal, R., Influence of Thermal Barrier Coating on Performance and Emission Characteristics of a Compression Ignition Engine Fueled with Delonix Regia Seed Biodiesel, J. Clean. Prod., 420, 138413 (2023).
https://doi.org/10.1016/j.jclepro.2023.138413
Lameski, P., Zdravevski, E., Trajkovik, V. and Kulakov, A., Weed detection dataset with RGB images taken under variable light conditions, Communications in Computer and Information Science, 778, 112-119 (2017).
https://doi.org/10.1007/978-3-319-67597-8_11
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P., Gradient-based learning applied to document recognition, Proc. IEEE, 86(11), 2278-2324 (1998).
https://doi.org/10.1109/5.726791
Leo, G. M. L., Chrispin, D. M., Jayabal, R., Murugapoopathi, S., Srinivasan, D. and Mukilarasan N., Experimental Evaluation and Neural Network Modelling of Reactivity-Controlled Compression Ignition Engine Using Cashew Nut Shell Oil Biodiesel-Alumina Nanoparticle Blend and Gasoline Injection, Energy, 282, 128923 (2023).
https://doi.org/10.1016/j.energy.2023.128923
Mallah, C., Cope, J. and Orwell, J., Plant leaf classification using probabilistic integration of shape, texture and margin features, Signal Processing, Pattern Recognition and Applications - 2013, 3842 (2013).
https://dx.doi.org/10.2316/P.2013.798-098
Minervini, M., Fischbach, A., Scharr, H. and Tsaftaris, S. A., Finely-grained annotated datasets for image-based plant phenotyping, Pattern Recognit. Lett., 81, 80-89 (2016).
https://doi.org/10.1016/j.patrec.2015.10.013
Mohanrajhu, N., Sekar, S, Jayabal, R. and Sureshkumar, R., Screening Nano Additives for Favorable NOx/Smoke Emissions Trade-off in a CRDI Diesel Engine Fueled by Industry Leather Waste Fat Biodiesel Blend, Process Saf. Environ. Prot., 187, 332–42 (2024).
https://doi.org/10.1016/j.psep.2024.04.115
Mohanty, S. P., Hughes, D. P. and Salathé, M., Using deep learning for image-based plant disease detection, Front. Plant Sci., 7, 1-10 (2016).
https://doi.org/10.3389/fpls.2016.01419
Olsen, A., Konovalov, D. A., Philippa, B., Ridd, P., Wood, J. C., Johns, J. and White, R. D., Deep Weeds: A multiclass weed species image dataset for deep learning, Sci. Rep., 9(1), 1-12 (2019).
https://doi.org/10.1038/s41598-018-38343-3
Siddiqi, M. H., Ahmad, I. and Sulaiman, S. B., Weed recognition based on erosion and dilation segmentation algorithm, 2009 International Conference on Education Technology and Computer, (2009).
http://dx.doi.org/10.1109/ICETC.2009.62
Sudars, K., Jasko, J., Namatevs, I., Ozola, L. and Badaukis, N., Dataset of annotated food crops and weed images for robotic computer vision control, Data Brief, 31, 105833 (2020).
https://doi.org/10.1016/j.dib.2020.105833
Wang, A., Zhang, W. and Wei, X., A review on weed detection using ground-based machine vision and image processing techniques, Comput. Electron. Agric., 158, 226-240 (2019).
http://dx.doi.org/10.1016/j.compag.2019.02.005
Yu, J., Schumann, A. W., Cao, Z., Sharpe, S. M., and Boyd, N. S., Weed detection in perennial ryegrass with deep learning convolutional neural network, Front. Plant Sci., 10, 1422 (2019b).
https://doi.org/10.3389/fpls.2019.01422
Yu, J., Sharpe, S. M., Schumann, A. W. and Boyd, N. S., Deep learning for image-based weed detection in turfgrass, Eur. J. Agron., 104, 78-84, (2019a).
