An artificial intelligence based approach for increasing agricultural yield

Naidila Sadashiv; Ankur Garg; Avinash Kumar; Ashwani Verma; Chandan Adhikari; S M Dilip Kumar    nbsp

doi:10.17485/IJST/v14i1.1977

Article

An artificial intelligence based approach for increasing agricultural yield

VIEWS 2368
PDF 607

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i1.1977

Year: 2021, Volume: 14, Issue: 1, Pages: 8-21

Original Article

An artificial intelligence based approach for increasing agricultural yield

Naidila Sadashiv^1*, Ankur Garg², Avinash Kumar², Ashwani Verma², Chandan Adhikari², S M Dilip Kumar³

¹Associate Professor, Department of CSE, JSS Academy of Technical Education, Bangalore, 560060, India. Tel.: +91-9900114849
²Department of CSE, JSS Academy of Technical Education, Bangalore, 560060, India
³Professor, Department of CSE, University Visvesvaraya College of Engineering, Bangalore, 560001, India

*Corresponding Author
Tel: +91-9900114849
Email: [email protected]

Received Date:09 November 2020, Accepted Date:20 December 2020, Published Date:11 January 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: Identification of a suitable crop based on soil and climatic condition along with plant disease detection are very much essential as they boost the agricultural yield. Monitoring these parameters is not largely carried out as they are very laborious and require expertise in the field, thus curtails the overall productivity. To address these challenges an artificial intelligence based techniques were applied to predict a suitable crop and also to detect the plant leaf disease at an early stage are been presented. Methods/Statistical analysis: Crop predictions are carried out by leveraging two machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM) on the agriculture dataset. Convolution Neural Network (CNN) with ResNet152 architecture is used for the detection of plant disease. An open dataset of 54,306 photos of healthy and diseased crops are considered during the performance evaluation. Findings: Crop predictions based on LR and SVM have achieved an accuracy of 93% and 97% respectively across a class of 13 crops. CNN based model predicts disease among 38 different categories from 14 specific crops with an accuracy of 96%. Novelty/Applications: The proposed approach will be beneficial to farmers to identify suitable crops and have plant leaf disease under control.

Keywords: CNN; crop prediction; leaf disease detection; logistic regression; machine learning; SVM

References

VS, Poovammal E. Prediction of Crop Yield using Regression Analysis. Indian Journal of Science and Technology. 2016;9(38):1–5. Available from: https://dx.doi.org/10.17485/ijst/2016/v9i38/91714
Kumar KK, Kumar KR, Ashrit RG, Deshpande NR, Hansen JW. Climate impacts on Indian agriculture. International Journal of Climatology. 2004;24(11):1375–1393. Available from: https://dx.doi.org/10.1002/joc.1081
Kumar R, Singh MP, Kumar P, Singh JP. Crop Selection Method to maximize crop yield rate using machine learning technique. In: 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). (pp. 138-145) 2015.
Rehman TU, Mahmud MS, Chang YK, Jin J, Shin J. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture. 2019;156:585–605. Available from: https://dx.doi.org/10.1016/j.compag.2018.12.006
Shreyas J, Kumar SMD. A Survey on Computational Intelligence Techniques for Internet of Things. International Conference on Communication and Intelligent Systems. 2020;p. 271–282. Available from: https://DOI.org/10.1007/978-981-15-3325-9_21
Srinidhi NN, Sagar CS, Chethan SD, Shreyas J, Kumar D, S. Machine Learning Based Efficient Multi-copy Routing for OppIoT Networks. Advances in Computational Intelligence, Security and Internet of Things. 2020;p. 288–302. Available from: https://DOI.org/10.1007/978-981-15-3666-3_24
Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review. 2020;53(8):5455–5516. Available from: https://dx.doi.org/10.1007/s10462-020-09825-6
Snehal, Dahikar S, Rode VS. Agricultural Crop Yield Prediction Using Artificial Neural Network Approach”. International Journal of Innovative Research in Electrical, Electronic, Instrumentation and Control Engineering. 2014;2(1). Available from: https://www.semanticscholar.org/paper/Agricultural-Crop-Yield-Prediction-Using-Artificial-Dahikar-Rode/aa36006afdd0ee0c9d08a13684d4e6255d6c63bc
AP, Purohit S, SJ, Shah K. Optimum Crop Prediction using Data Mining and Machine Learning techniques”. International Journal for Research in Applied Science & Engineering Technology. 2014. Available from: https://doi.org/10.22214/ijraset.2019.3436
Nagini R, Rajnikanth TV, Kiranmayee BV. Agriculture Yield Prediction Using Predictive Analytic Techniques. In: 2nd International Conference on Contemporary Computing and Informatics. .
Gandhi N, Armstrong LJ. Owaiz petkar Amiya Kumar Tripathy, “Rice Crop Yield Prediction in India using Support Vector Machines. International Joint Conference on Computer Science and Software Engineering. 2016;p. 1–5. Available from: https://doi.org/10.1109/JCSSE.2016.7748856
Dhaygude [B, Kumbhar NP. Agricultural plant Leaf Disease Detection Using Image Processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2013;2(1). Available from: https://www.ijareeie.com/upload/january/5_Agricultural%20plant.pdf
Madhgoria S, Marekschikora W, Koch. Pixel-Based Classification Method for Detecting Unhealthy Regions in Leaf Images. 2009. Available from: https://www.semanticscholar.org/paper/Pixel-based-classification-method-for-detecting-in-adhogariaSchikora/9ae48c2979651f5163321837d8ecd8bc27d8ca51
Ramesh S, Niveditha M, Pooja R, Bhat P, Shashank N, N, et al. Plant Disease Detection Using Machine Learning. In: International Conference on Design Innovations for 3Cs Compute Communicate Control. .
Jadhav SB, Udupi VR, Patil SB. Identification of plant diseases using convolutional neural networks. International Journal of Information Technology. 2020. Available from: https://dx.doi.org/10.1007/s41870-020-00437-5
Jiang P, Chen Y, Liu B, He D, Liang C. Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks. IEEE Access. 2019;7:59069–59080. Available from: https://dx.doi.org/10.1109/access.2019.2914929
Kirchner A, Signorino CS. Using Support Vector Machines for Survey Research. Survey Practice . 2018;11(1):169–173. Available from: https://doi.org/10.29115/SP-2018-0001
Revathi P, Hema M, Latha. Classification Of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques”. International Journal of Applied Research . 2012;p. 169–173. Available from: https://doi.org/10.1109/INCOSET.2012.6513900
Khairnar K, Dagade R. Disease Detection and Diagnosis on Plant using Image Processing A Review. International Journal of Computer Applications. 2014;108(13):36–38. Available from: https://dx.doi.org/10.5120/18973-0445
Department of Agriculture, Cooperation and Welfare. Available from: http://agricoop.nic.in (accessed 2020-02-20)
. Kaggle Datasets. Available from: https://www.kaggle.com/abdallahalidev/plantvillage-dataset (accessed 2020-03-15)
Thapa R, Zhang K, Snavely N, Belongie S, Khan A. The Plant Pathology Challenge 2020 data set to classify foliar disease of apples. Applications in Plant Sciences. 2020;8(9). Available from: https://dx.doi.org/10.1002/aps3.11390

Copyright

© 2021 Sadashiv et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)