Current Science

Open Access Open Access  Restricted Access Subscription Access

Machine Learning Algorithms for Categorization Of Agricultural Dust Emissions Using Image Processing of Wheat Combine Harvester


Affiliations
1 ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., India
2 ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India., India
 

India is the second largest wheat producer in the world after Russia. Wheat harvesting in the country was traditionally done using a sickle, a hand tool. However, in the last two decades, combined harvesters have been extensively used. The rapid development of mech­anization has resulted in the production of dust and straw particles during the harvesting operation of wheat. These particles have severe health hazards for the machine operator. Exposure to various types of particulate matter has a variety of effects on human health. Such an effect can be minimized if the concentration of the generated particle is maintained within a permissible limit. Hence, the present study has been conducted to evaluate and categorize dust and straw particles in the workspace of a combine harvester operator during wheat harvesting. An image-processing technique was used to study a field data sample collected on sticky paper. It describes a novel method of collecting dust and straw particles while harvesting wheat. Few studies have been conducted in developing countries to analyse the characteristics of dust and wheat straw exposure of combined harvester operators. The number of dust and straw particles deposited per square millimetre was 9–12, with sizes ranging from 10 to 1400 mm. The extracted data were divided into three groups, viz. thoracic, inhalable and straw and modelled using machine learning algorithms, including support vector machine (SVM) and k-nearest neighbor. With an accuracy of 96%, SVM outperformed the other methods for categorising dust and straw particles, whereas linear discriminant analysis performed poorly with an accuracy of 88%.

Keywords

Agriculture, Combine Harvester, Dust and Straw Particles, Image Processing, Machine Learning.
User
Notifications
Font Size

  • Berglund, B., Lindvall, T. and Schwela, D. H., World Health Organiza-tion Occupational and Environmental Health Team. Guidelines for Community Noise, World Health Organization, Geneva, Switzer-land, 1999.

  • Devi, M., Kumar, J., Malik, D. P. and Mishra, P., Forecasting of wheat production in Haryana using hybrid time series model. J. Agric. Food Res., 2021, 5, 100175.

  • Singh, S. P. and Singh, S., Farm power availability and its perspec-tive in Indian agriculture. RASSA J. Sci. Soc., 2021, 3(2), 114–126.

  • Mehta, C. R., Chandel, N. S., Jena, P. C. and Jha, A., Indian agri-culture counting on farm mechanization. Agric. Mech. Asia, Afr. Latin Am., 2019, 50(1), 84–89.

  • Zander, J., Ergonomics in machine design: a case-study of the self-propelled combine harvester, Doctoral dissertation, Wageningen University of Research, Wageningen, 1972.

  • Kirkhorn, S. R. and Garry, V. F., Agricultural lung diseases. Envi-ron. Health Perspect., 2000, 108(4), 705–712.

  • Suggs, C. W., Thermal Environment of Agricultural Workers: Envi-ronmental Stress, Module 10, ASAE, Michigan, USA, 1991.

  • Nieuwenhuijsen, M. J., Noderer, K. S., Schenker, M. B., Vally-athan, V. and Olenchock, S., Personal exposure to dust endotoxin and crystalline silica in California agriculture. Ann. Occup. Hyg., 1999, 43(1), 35–42.

  • Atiemo, M. A., Yoshida, K. and Zoerb, G. C., Dust measurements in tractor and combine cabs. Trans. ASAE, 1980, 23(3), 0571–0576.

  • Becklake, M. R., Grain dust and lung health: not just a nuisance dust. Can. Respir. J., 2007, 14(7), 423–425.

  • Zock, J. P., Heederik, D. and Hans, K., Exposure to dust, endotoxin and micro-organisms in the potato processing industry. Ann. Occup. Hyg., 1995, 39(6), 841–854.

  • Ekka, U., Kumar, A. and Roy, H. S., Particulate matter exposure of combine harvester operator during wheat harvesting in northern India. Indian J. Agric. Sci., 2021, 91(5), 678–682.

  • Siomos, N., Fountoulakis, I., Natsis, A., Drosoglou, T. and Bais, A., Automated aerosol classification from spectral UV measurements using machine learning clustering. Remote Sensing, 2020, 12(6), 965.

  • Du, Y., Xu, X., Chu, M., Guo, Y. and Wang, J., Air particulate matter and cardiovascular disease: the epidemiological, biomedical and clinical evidence. J. Thorac. Dis., 2016, 8(1), E8.

  • Behera, D., Pal, D. and Gupta, D., Respiratory symptoms among farmers in the vicinity of a north Indian city. Lung India, 2005, 22, 45–49.

  • Swan, J. R. M., Blainey, D. and Crook, B., The HSE grain dust study – workers’ exposure to grain dust contaminants, immunological and clinical response. Health and Safety Executive Research Re-port, 2007, p. 540.

  • Matthews, J. and Knight, A. A., Ergonomics in Agricultural Equipment Design, National Institute of Agricultural Engineering, Silsoe, UK, 1973.

  • L’heureux, A., Grolinger, K., Elyamany, H. F. and Capretz, M. A., Machine learning with big data: challenges and approaches. IEEE Access, 2017, 5, 7776–7797.

  • Qiu, J., Wu, Q., Ding, G., Xu, Y. and Feng, S., A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process., 2016, 2016(1), 1–16.

  • Shalev-Shwartz, S. and Ben-David, S. Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, New York, USA, 2014.

  • Gomes, L. C., Faria, R. M., de Souza, E., Veloso, G. V., Schaefer, C. E. G. and Fernandes Filho, E. I., Modelling and mapping soil organic carbon stocks in Brazil. Geoderma, 2019, 340, 337–350.

  • Keskin, H., Grunwald, S. and Harris, W. G., Digital mapping of soil carbon fractions with machine learning. Geoderma, 2019, 339, 40–58.

  • Khaledian, Y. and Miller, B. A., Selecting appropriate machine learning methods for digital soil mapping. Appl. Math. Model., 2020, 81, 401–418.

  • Tong, W., Machine learning for spatiotemporal big data in air pollution. In Spatiotemporal Analysis of Air Pollution and Its Application in Public Health, Elsevier, Amsterdam, The Netherlands, 2020, pp. 107–134.

  • Masood, A. and Ahmad, K., A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches. Procedia Comput. Sci., 2020, 167, 2101–2110.

  • Pham, B. T., Prakash, I., Singh, S. K., Shirzadi, A., Shahabi, H. and Bui, D. T., Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. Catena, 2019, 175, 203–218.

  • Rahmati, O. et al., Hybridized neural fuzzy ensembles for dust source modeling and prediction. Atmos. Environ., 2020, 224, 117320.

  • Ebrahimi, K. Z., Roustaei, F., Khusfi, M. E. and Naghavi, S., In-vestigation of the relationship between dust storm index, climatic parameters, and normalized difference vegetation index using the ridge regression method in arid regions of Central Iran. Arid Land Res. Manage., 2020, 34, 239–263.

  • Czernecki, B., Marosz, M. and Jędruszkiewicz, J., Assessment of machine learning algorithms in short-term forecasting of PM10 and PM2.5 concentrations in selected polish agglomerations. Aerosol Air Qual. Res., 2021, 21, 200586.

  • Lu, W. Z. and Wang, W. J., Potential assessment of the ‘support vector machine’ method in forecasting ambient air pollutant trends. Chemosphere, 2005, 59(5), 693–701.

  • Osowski, S. and Garanty, K., Forecasting of the daily meteorologi-cal pollution using wavelets and support vector machine. Eng. Appl. Artif. Intell., 2007, 20(6), 745–755.

  • Lee, J., Shi, Y. R., Cai, C., Ciren, P., Wang, J., Gangopadhyay, A. and Zhang, Z., Machine learning based algorithms for global dust aerosol detection from satellite images: inter-comparisons and evaluation. Remote Sensing, 2021, 13(3), 456.

  • Xiong, R. and Tang, P., Machine learning using synthetic images for detecting dust emissions on construction sites. Smart Sustain. Built Environ., 2021, 10(3), 487–503.

  • Friedl, M. A. and Brodley, C. E., Decision tree classification of land cover from remotely sensed data. Remote Sensing Environ., 1997, 61(3), 399–409.

  • Chakma, A., Vizena, B., Cao, T., Lin, J. and Zhang, J., Image-based Air Quality Analysis using Deep Convolutional Neural Network, International Conference on image processing, Institute of Electrical and Electronics Engineers, Beijing, China, 2017, pp. 3949–3952.

  • Guo, G., Wang, H., Bell, D., Bi, Y. and Greer, K., KNN model-based approach in classification. In OTM Confederated International Conferences: On the Move to Meaningful Internet Systems, Springer, Berlin, Germany, 2003, pp. 986–996.

  • Zhang, M. L., Peña, J. M. and Robles, V., Feature selection for multi-label naive Bayes classification. Inf. Sci., 2009, 179(19), 3218–3229.

  • Spankie, S. and Cherrie, J. W., Exposure to grain dust in Great Britain. Ann. Occup. Hyg., 2012, 56(1), 25–36.

  • Huy, T., De Schipper, K., Chan-Yeung, M. and Kennedy, S. M., Grain dust and lung function. Dose–response relationships. Am. Rev. Resp. Dis., 1991, 144(6), 1314–1321.

  • Baker, J. B., Southard, R. J. and Mitchell, J. P., Agricultural dust production in standard and conservation tillage systems in the San Joaquin Valley. J. Environ. Qual., 2005, 34, 1260–1269.

  • Armentia, A., Martinez, A. and Castrodeza, R., Occupational allergic disease in cereal workers by stored grain pests. J. Asthma, 1997, 34, 369–378.

  • Chan, Y. M., Dimich, W. H. and Enarson, D. A., Five cross-sectional studies of grain elevator workers. Am. J. Ind. Med., 1992, 136, 1269–1279.

  • Chan-Yeung, M., Schulzer, M., MacLean, L., Dorken, E. and Grzyb-owski, S., Epidemiologic health survey of grain elevator workers in British Columbia. Am. Rev. Resp. Dis., 1980, 121(2), 329–338.


Abstract Views: 116

PDF Views: 68




  • Machine Learning Algorithms for Categorization Of Agricultural Dust Emissions Using Image Processing of Wheat Combine Harvester

Abstract Views: 116  |  PDF Views: 68

Authors

Utpal Ekka
ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., India
Himadri Shekhar Roy
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India., India
Adarsh Kumar
ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., India
S. P. Singh
ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., India
Apratim Kumar Pandey
ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., India
Kamalika Nath
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India., India

Abstract


India is the second largest wheat producer in the world after Russia. Wheat harvesting in the country was traditionally done using a sickle, a hand tool. However, in the last two decades, combined harvesters have been extensively used. The rapid development of mech­anization has resulted in the production of dust and straw particles during the harvesting operation of wheat. These particles have severe health hazards for the machine operator. Exposure to various types of particulate matter has a variety of effects on human health. Such an effect can be minimized if the concentration of the generated particle is maintained within a permissible limit. Hence, the present study has been conducted to evaluate and categorize dust and straw particles in the workspace of a combine harvester operator during wheat harvesting. An image-processing technique was used to study a field data sample collected on sticky paper. It describes a novel method of collecting dust and straw particles while harvesting wheat. Few studies have been conducted in developing countries to analyse the characteristics of dust and wheat straw exposure of combined harvester operators. The number of dust and straw particles deposited per square millimetre was 9–12, with sizes ranging from 10 to 1400 mm. The extracted data were divided into three groups, viz. thoracic, inhalable and straw and modelled using machine learning algorithms, including support vector machine (SVM) and k-nearest neighbor. With an accuracy of 96%, SVM outperformed the other methods for categorising dust and straw particles, whereas linear discriminant analysis performed poorly with an accuracy of 88%.

Keywords


Agriculture, Combine Harvester, Dust and Straw Particles, Image Processing, Machine Learning.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi9%2F1074-1081