home icon user icon
Welcome to International Network for Natural Sciences | INNS Pub.
Home My Account
Submit Manuscript
double_slash
breadcumb_bg

Artificial intelligence driven smart agricultural applica

Paper Details

Research Paper 10/11/2023
Views (380) Download (68)
current_issue_feature_image
publication_file

Artificial intelligence driven smart agricultural applica

Rubul Kumar Bania, Satyajit Sarmah
Int. J. Biosci.23( 5), 142-151, November 2023.
Certificate: IJB 2023 [Generate Certificate]
Key: AgrotechnologyArtificial intelligenceMachine learningSmart agriculture

Abstract

Agriculture serves as a cornerstone of the economy and a significant source of employment, particularly in developing countries like India. Within the realm of agriculture, there are three pivotal stages: pre-harvesting, harvesting, and post-harvesting. The emerging field of agrotechnology, often referred to as smart or digital agriculture, leverages data-intensive approaches to enhance agricultural productivity while minimizing its ecological footprint. Notably, the demand for artificial intelligence (AI) which includes machine learning (ML) and deep learning (DL) technologies has surged within the agrotechnology sector. This paper offers a comprehensive review of the latest applications of AI in agriculture, aiming to address challenges in the pre-harvesting, harvesting, and post-harvesting phases. It explores how AI and ML models can significantly assist farmers in decision-making processes across various domains and applications such as soil management, weed management, crop management, livestock management, water management etc. The findings of this study underscore the remarkable outcomes achieved by ML algorithms and models in resolving agricultural issues. In light of these findings, it is recommended that ML models be deployed in various real-time applications to provide valuable support to intended users, particularly farmers, in their day-to-day agricultural endeavours.

VIEWS 45
Cover PDF

Khanna A, Kaur S. 2019. Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture 157 (34), 218–231. https:// doi.org/10.1016/j.compag.2018.12.039.

Burra D.D, Hildebrand J, Giles J, Nguyen T, Hasiner. 2021. Digital Agriculture Profile: Viet Nam (Report). Food and Agriculture Organization of the United Nations.

Dhanya VG,  Subeesh A. 2022. Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture 6 (1), 211–229.

Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556.

Bania KR. 2023. Ensemble of deep transfer learning models for real-time automatic detection of face mask. Multimedia Tools and Applications 82, pages 25131–25153. https://doi.org/10.1007/s11042-023-14408-y

Khamparia A, Saini G, Gupta D, Khanna A, Tiwari S. 2019. Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network. Circuits System, and Signal Process  39, 818–836.

M. Islam, Anh Dinh, Wahid K, Bhowmik, P. 2017. Detection of potato diseases using image segmentation and multiclass support vector machine, IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada, pp. 1-4. doi: 10.1109/CCECE.2017.7946594

Samajpati BJ, Degadwala S. 2016. Hybrid approach for apple fruit disease detection and classification using random forest classifier. International conference on communication and signal processing (ICCSP). DOI: 10.1109/ICCSP.2016.7754302

Zhu S, Zhou L, Gao P, Bao. 2019. Near-infrared hyperspectral imaging combined with deep learning to identify cotton seed varieties.  Molecules 24, 3268. https://doi.org/10.3390/molecules24183268.

Li C, Li H, Liu Z, Li B, Huang Y. 2021. SeedSortNet: a rapid and highly effificient lightweight CNN based on visual attention for seed sorting. PeerJ Computer Science 7, e639. https://doi.org/10.7717/peerj-cs.639.

Zhao G, Quan L, Li H, Feng H, Li S. 2021. Real-time recognition system of soybean seed full-surface defects based on deep learning. Computers and Electronics in Agriculture 187, 106230. https://doi.org/10.1016/j.compag.2021.106230

Taheri-Garavand A, Nasiri A, Fanourakis D, Fatahi S, Omid M, Nikoloudakis N. 2021. Automated in situ seed variety identification via deep learning: a case study in chickpea. Plants 10, 1406. https://doi.org/10.3390/plants10071406.

Jia HY, Chen J, Yu HL, Liu DY. 2010. Soil fertility grading with Bayesian Network transfer learning. In proceedings of  IEEE International conference on machine learning and cybernetics 3, 1159–63. DOI: 10.1109/ICMLC.2010.5580915

Reashma SJ, Pillai AS. 2017. Edaphic factors and crop growth using machine learning—A review. In: International conference on intelligent sustainable systems (ICISS). IEEE,  270–274.

Sirsat MS, Cernadas E, Ferna´ndez-Delgado M, Khan R. 2017. Classification of agricultural soil parameters in India. Computers and Electronics in Agriculture 135(6), 269–279.

Glória A, Cardoso J, Sebastião P. 2021. Sustainable irrigation system for farming supported by machine learning and real-time sensor data. Sensors 21, 3079.

Zhang S, Li S, Ke Z, Zhao H, Guo, Y. 2021. Investigation on environment monitoring system for a combination of hydroponics and aquaculture in greenhouse. Information Processing in Agriculture 13(2), 12-19.

Ahmed F, Al-Mamun HA, Hossain Bari ASM, Hossain E, Kwan P, 2012. Classification of crops and weeds from digital images: a support vector machine approach Crop Protect, 40, 98–104, https://doi.org/10.1016/J.CROPRO.2012.04.024.

Zheng Y, Zhu Q.  2017. Maize and weed classification using color indices with support vector data description in outdoor fields, Computers and Electronics in Agriculture 141, 215–222. https://doi.org/10.1016/j. compag.2017.07.028.

Hoang Trong V, Gwang-hyun Y, Thanh VuD, Jin-young K. 2020. Late fusion of multimodal deep neural networks for weeds classification. Computers and Electronics in Agriculture 175, 105506. DOI: 10.1016/j.compag.2020.105506.

Qiu Z, Ma F, Li Z, Xu X, Ge H, Du C. 2021. Estimation of nitrogen nutrition index in rice from uav rgb images coupled with machine learning algorithms. Computers and Electronics in Agriculture 189, 106421. DOI: 10.1016/j.compag.2021.106421

Backhaus A, Bollenbeck F, Seiffert, U. 2011. Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. 3rd  Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 1–4.

Ghosal S, Blystone D, Singh AK. 2018. An explainable deep machine vision framework for plant stress phenotyping. Proc. Natlional. Academy Science 115, 4613–4618.

Kumar S, Singh SK, Dutta T, Gupta HP.  2016. A fast cattle recognition system using smart devices. Proceedings of the 24th  ACM International Conference on Multimedia, pp. 742–743. DOI: 10.1145/2964284.2973829.

Zhang C, Yue P, Di L, Wu Z. 2018. Automatic identification of center pivot irrigation systems from landsat images using convolutional neural networks. Agriculture 8, 147. https://doi.org/10.3390/agriculture8100147.

Hansen, M.F, Smith, M.L, Smith, L.N, Salter. 2018. Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry98, 145–152. https://doi.org/10.1016/j.compind.2018.02.016.

Huzzey JM, Veira DM. 2007. Prepartum behavior and dry matter intake identify dairy cows at risk for metritis. Jurnal Dairy Science 90, 3220–3233. https://doi.org/10.3168/jds.2006-807.

Bania RK, Dey S, Buragohain N. 2023. Hybrid ResNet50-PCA based deep transfer learning approach for classification of tomato leaf diseases. Journal of Biodiversity and Environment Science 42 (2), 42-48.

C. Szegedy et al., 2015. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1-9. DOI: 10.1109/CVPR.2015.7298594.

Submit Manuscript