Main Article Content
Abstract
The demands of population are gradually increasing rapidly. Most manufacturing and processing industries face obstacles or problems to fulfil this daily increase in demand. Even if the demand can be managed, the transmission of diseases through these sectors is also a considerable concern. Many new technologies are being used to avoid unbearable consequences, which arise because of human-to-human contact or interaction. Higher firms adopt a ton of computer software and other modern technologies to get rid of such problems. Farming, one among the essential professions is also being impacted; however, human beings have come a long way since they started farming. This paper examines how one of the latest technologies, Artificial Intelligence, has helped improve farming, agriculture, and related tasks to increase yield and productivity. How Animal Farming, Detection of Diseases in Plants, Grading processes of Agricultural Foods, and quality check of vegetables and fruits, have advanced with the aid of Artificial Intelligence, is summed up in the subsequent sections.
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Copyright (c) 2021 Environment Conservation Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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- https://www.sciencedirect.com/science/article/pii/S2214799316300777
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- Iván Ramírez Morales, Daniel Rivero Cebrián, Enrique Fernández Blanco, Alejandro Pazos Sierra., 2016. Early warning in egg production curves from commercial hens: A SVM Approach. Computers and Electronics in Agriculture Volume 121, Pages 169-179. https://www.sciencedirect.com/science/article/pii/S0168169915003919
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- Jaime Alonso, Angel Rodriguez Castanon, Antonio Bahamonde., 2013. Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter. Computers and Electronics in Agriculture.Volume 91, Pages 116-120.
- https://www.sciencedirect.com/science/article/pii/S0168169912002232
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- Pilli, S.K., Nallathambi, B., George, S.J. and Diwanji, V., 2015, February. eAGROBOT—A robot for early crop disease detection using image processing. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1684-1689). IEEE. https://doi.org/10.1109/ECS.2014.7090754
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- Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J. and Hughes, D.P., 2017. Deep learning for image-based cassava disease detection. Frontiers in plant science, 8, p.1852. https://doi.org/10.3389/fpls.2017.0185
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- Ritaban Dutta, Daniel Smith, Richard Rawnsley, Greg Bishop-Hurley, James Hills, Greg Timms , Dave Henry. 2015. Dynamic cattle behavioural classification using supervised ensemble Classifiers. Computers and Electronics in Agriculture.Volume 111, Pages 18-28. https://www.sciencedirect.com/science/article/pii/S0168169914003123
- Roscher, R., Behmann, J., Mahlein, A.K., Dupuis, J., Kuhlmann, H. and Plümer, L., 2016. Detection of disease symptoms on hyperspectral 3d plant models. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3(7). https://doi.org/10.5194/isprs-annals-III-7-89-2016
- Sain, M., & Singh, R., Kaur, A. (2020). Robotic Automation in Dairy and Meat Processing Sector for Hygienic Processing and Enhanced Production. Journal of Community Mobilization and Sustainable Development, Vol. 15(3), 543-550, September-December, 2020
- Saleem, M.H., Potgieter, J. and Arif, K.M., 2019. Plant disease detection and classification by deep learning. Plants, 8(11), p.468. https://doi.org/10.3390/plants8110468
- Selvaraj, M.G., Vergara, A., Ruiz, H., Safari, N., Elayabalan, S., Ocimati, W. and Blomme, G., 2019. AI-powered banana diseases and pest detection. Plant Methods, 15(1), p.92.
- https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0475-z
- Singh, R., Sain, M., Singh, B., Nagi, H. S., & Bala, N. (2020). Development of a Cost Effective Beverage and Food-Serving Robot for Hygienically Outcomes and Human Comfort. International Journal of Curent Microbiology and Applied Science 9(5), 247-257.
- Stephen G. Matthews, Amy L. Miller, Thomas PlÖtz and Ilias Kyriazakis., 2017. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci Rep 7, 17582.
- https://doi.org/10.1038/s41598-017-17451-6.
- Tetila, E.C., Machado, B.B., Menezes, G.K., Oliveira, A.D.S., Alvarez, M., Amorim, W.P., Belete, N.A.D.S., Da Silva, G.G. and Pistori, H., 2019. Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 17(5), pp.903-907. https://doi.org/10.1109/LGRS.2019.2932385
- Thomas, S., Wahabzada, M., Kuska, M.T., Rascher, U. and Mahlein, A.K., 2017. Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Functional Plant Biology, 44(1), pp.23-34. https://doi.org/10.1071/FP16127
- Tibbets, John H., 2018. From identifying plant pests to picking fruit, AI is reinventing how farmers produce your food. https://www.eco-business.com/news/from-identifying-plant-pests-to-picking-fruit-ai-is-reinventing-how-farmers-produce-your-food
- Tm, P., Pranathi, A., SaiAshritha, K., Chittaragi, N.B. and Koolagudi, S.G., 2018. Tomato leaf disease detection using convolutional neural networks. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE. https://doi.org/10.1109/IC3.2018.8530532
- Vinicius Pegorini, Leandro Zen Karam, Christiano Santos Rocha Pitta, Rafael Cardoso, Jean Carlos Cardozo da Silva, Hypolito José Kalinowski, Richardson Ribeiro, Fábio Luiz Bertotti and Tangriani Simioni Assmann., 2015. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701289
- Win, T.T., 2018. AI and IoT methods for plant disease detection in Myanmar. Doctoral dissertation, Kobe Institute OF Computing. Master thesis, submitted to Markon lab department of information technology.
- Wu, H., Wiesner?Hanks, T., Stewart, E.L., DeChant, C., Kaczmar, N., Gore, M.A., Nelson, R.J. and Lipson, H., 2019. Autonomous detection of plant disease symptoms directly from aerial imagery. The Plant Phenome Journal, 2(1), pp.1-9.
- Zhu, H., Cen, H., Zhang, C. and He, Y., 2016. Early detection and classification of tobacco leaves inoculated with tobacco mosaic virus based on hyperspectral imaging technique. In 2016 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.20162460422.
References
Barbedo, J.G.A., 2019. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), p.40. https://doi.org/10.3390/drones3020040
Blackwell, A. F., 2002. What is Programming? University of Cambridge Computer Laboratory. https://ogur.org/1590284993.pdf
Chen, J., Chen, J., Zhang, D., Sun, Y. and Nanehkaran, Y.A., 2020. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, p.105393. https://doi.org/10.1016/j.compag.2020.105393
Cui, S., Ling, P., Zhu, H. and Keener, H.M., 2018. Plant pest detection using an artificial nose system: a review. Sensors, 18(2), p.378. https://doi.org/10.3390/s18020378
Fuentes, A., Yoon, S., Kim, S.C. and Park, D.S., 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), p.2022. https://doi.org/10.3390/s17092022
George-John E Nychas, Efstathios Z Panagou, Fady Mohareb., 2016. Novel approaches for food safety management and communication. Current Opinion in Food Science. Volume 12, December 2016, Pages 13-20
https://www.sciencedirect.com/science/article/pii/S2214799316300777
Grigorios Tsoumakas., 2018. Survey of machine learning techniques for food sales prediction. Artif Intell Rev 52, 441–447. https://doi.org/10.1007/s10462-018-9637-z
Iván Ramírez Morales, Daniel Rivero Cebrián, Enrique Fernández Blanco, Alejandro Pazos Sierra., 2016. Early warning in egg production curves from commercial hens: A SVM Approach. Computers and Electronics in Agriculture Volume 121, Pages 169-179. https://www.sciencedirect.com/science/article/pii/S0168169915003919
Jaime Alonso, Alfonso Villa, Antonio Bahamonde., 2015. Improved estimation of bovine weight trajectories using Support Vector Machine Classification. Computers and Electronics in Agriculture. Volume 110, Pages 36-41. https://www.sciencedirect.com/science/article/pii/S0168169914002488
Jaime Alonso, Angel Rodriguez Castanon, Antonio Bahamonde., 2013. Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter. Computers and Electronics in Agriculture.Volume 91, Pages 116-120.
https://www.sciencedirect.com/science/article/pii/S0168169912002232
Jiang, P., Chen, Y., Liu, B., He, D. and Liang, C., 2019. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 7, pp.59069-59080.
Liu, B., Zhang, Y., He, D. and Li, Y., 2018. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), p.11. https://doi.org/10.3390/sym10010011
Marinoudi, V., Sørensen, C. G., Pearson, S., & Bochtis, D., 2019. Robotics and labour in agriculture. A context consideration. Biosystems Engineering, 184, 111–121. doi:10.1016/j.biosystemseng.2019.06.013
Mark F. Hansena, Melvyn L. Smitha, Lyndon N. Smitha, Michael G. Salterb, Emma M. Baxterc, Marianne Farishc, Bruce Grieved., 2018. Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry.Volume 98, June 2018, Pages 145-152. https://www.sciencedirect.com/science/article/pii/S0166361517304992
M. Craninxa, V. Fieveza, B. Vlaemincka, B. De Baetsb., 2008. Artificial neural network models of the rumen fermentation pattern in dairy cattle. Computers and Electronics in Agriculture 60(2):226-238. https://www.researchgate.net/publication/250718986_Artificial_neural_network_models_of_the_rumen_fermentation_pattern_in_dairy_cattle
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M., 1983. Machine Learning published by Springer.
https://link.springer.com/content/pdf/10.1007/978-3-662-12405-5.pdf
Moghadam, P., Ward, D., Goan, E., Jayawardena, S., Sikka, P. and Hernandez, E., 2017, November. Plant disease detection using hyperspectral imaging. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/abstract/document/8227476
Mokhtar, U., Ali, M.A., Hassanien, A.E. and Hefny, H., 2015. Identifying two of tomatoes leaf viruses using support vector machine. In Information Systems Design and Intelligent Applications (pp. 771-782). Springer, New Delhi. DOI: 10.1007/978-81-322-2250-7_77
Nagasubramanian, K., Jones, S., Singh, A.K., Sarkar, S., Singh, A. and Ganapathysubramanian, B., 2019. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant methods, 15(1), p.98. https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0479-8
N.Elakkiya, Dr.S.Karthikeyan, T.Ravi., 2018. Survey of grading process for agricultural foods by using Artificial Intelligence Technique. https://ieeexplore.ieee.org/abstract/document/8474663/
Pilli, S.K., Nallathambi, B., George, S.J. and Diwanji, V., 2015, February. eAGROBOT—A robot for early crop disease detection using image processing. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1684-1689). IEEE. https://doi.org/10.1109/ECS.2014.7090754
Prince, G., Clarkson, J.P. and Rajpoot, N.M., 2015. Automatic detection of diseased tomato plants using thermal and stereo visible light images. PloS one, 10(4), p.e0123262. https://doi.org/10.1371/journal.pone.0123262
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J. and Hughes, D.P., 2017. Deep learning for image-based cassava disease detection. Frontiers in plant science, 8, p.1852. https://doi.org/10.3389/fpls.2017.0185
Ranjan, M., Weginwar, M.R., Joshi, N. and Ingole, A.B., 2015. Detection and classification of leaf disease using artificial neural network. International Journal of Technical Research and Applications, 3(3), pp.331-333. https://www.ijtra.com
Ritaban Dutta, Daniel Smith, Richard Rawnsley, Greg Bishop-Hurley, James Hills, Greg Timms , Dave Henry. 2015. Dynamic cattle behavioural classification using supervised ensemble Classifiers. Computers and Electronics in Agriculture.Volume 111, Pages 18-28. https://www.sciencedirect.com/science/article/pii/S0168169914003123
Roscher, R., Behmann, J., Mahlein, A.K., Dupuis, J., Kuhlmann, H. and Plümer, L., 2016. Detection of disease symptoms on hyperspectral 3d plant models. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3(7). https://doi.org/10.5194/isprs-annals-III-7-89-2016
Sain, M., & Singh, R., Kaur, A. (2020). Robotic Automation in Dairy and Meat Processing Sector for Hygienic Processing and Enhanced Production. Journal of Community Mobilization and Sustainable Development, Vol. 15(3), 543-550, September-December, 2020
Saleem, M.H., Potgieter, J. and Arif, K.M., 2019. Plant disease detection and classification by deep learning. Plants, 8(11), p.468. https://doi.org/10.3390/plants8110468
Selvaraj, M.G., Vergara, A., Ruiz, H., Safari, N., Elayabalan, S., Ocimati, W. and Blomme, G., 2019. AI-powered banana diseases and pest detection. Plant Methods, 15(1), p.92.
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0475-z
Singh, R., Sain, M., Singh, B., Nagi, H. S., & Bala, N. (2020). Development of a Cost Effective Beverage and Food-Serving Robot for Hygienically Outcomes and Human Comfort. International Journal of Curent Microbiology and Applied Science 9(5), 247-257.
Stephen G. Matthews, Amy L. Miller, Thomas PlÖtz and Ilias Kyriazakis., 2017. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci Rep 7, 17582.
https://doi.org/10.1038/s41598-017-17451-6.
Tetila, E.C., Machado, B.B., Menezes, G.K., Oliveira, A.D.S., Alvarez, M., Amorim, W.P., Belete, N.A.D.S., Da Silva, G.G. and Pistori, H., 2019. Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 17(5), pp.903-907. https://doi.org/10.1109/LGRS.2019.2932385
Thomas, S., Wahabzada, M., Kuska, M.T., Rascher, U. and Mahlein, A.K., 2017. Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Functional Plant Biology, 44(1), pp.23-34. https://doi.org/10.1071/FP16127
Tibbets, John H., 2018. From identifying plant pests to picking fruit, AI is reinventing how farmers produce your food. https://www.eco-business.com/news/from-identifying-plant-pests-to-picking-fruit-ai-is-reinventing-how-farmers-produce-your-food
Tm, P., Pranathi, A., SaiAshritha, K., Chittaragi, N.B. and Koolagudi, S.G., 2018. Tomato leaf disease detection using convolutional neural networks. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE. https://doi.org/10.1109/IC3.2018.8530532
Vinicius Pegorini, Leandro Zen Karam, Christiano Santos Rocha Pitta, Rafael Cardoso, Jean Carlos Cardozo da Silva, Hypolito José Kalinowski, Richardson Ribeiro, Fábio Luiz Bertotti and Tangriani Simioni Assmann., 2015. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701289
Win, T.T., 2018. AI and IoT methods for plant disease detection in Myanmar. Doctoral dissertation, Kobe Institute OF Computing. Master thesis, submitted to Markon lab department of information technology.
Wu, H., Wiesner?Hanks, T., Stewart, E.L., DeChant, C., Kaczmar, N., Gore, M.A., Nelson, R.J. and Lipson, H., 2019. Autonomous detection of plant disease symptoms directly from aerial imagery. The Plant Phenome Journal, 2(1), pp.1-9.
Zhu, H., Cen, H., Zhang, C. and He, Y., 2016. Early detection and classification of tobacco leaves inoculated with tobacco mosaic virus based on hyperspectral imaging technique. In 2016 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.20162460422.