Use of Machine Learning to Identify Plant Disease: A Review

Authors

  • Antima Jain Student, Department of Advanced Computer Science, Poornima College of Engineering (RTU, Kota Affiliated), Jaipur, Rajasthan, India
  • Rahul Jindal Student, Department of Advanced Computer Science, Poornima College of Engineering (RTU, Kota Affiliated), Jaipur, Rajasthan, India
  • Gaurav Sharma Assistant Professor, Department of Advanced Computer Science, Poornima College of Engineering (RTU, Kota Affiliated), Jaipur, Rajasthan, India
  • Rishabh Sharma Student, Department of Advanced Computer Science, Poornima College of Engineering (RTU, Kota Affiliated), Jaipur, Rajasthan, India
  • Gaurav Jain Student, Department of Advanced Computer Science, Poornima College of Engineering (RTU, Kota Affiliated), Jaipur, Rajasthan, India
  • Rishika Jain Student, Department of Advanced Computer Science, Poornima College of Engineering (RTU, Kota Affiliated), Jaipur, Rajasthan, India

Keywords:

Plant diseases, machine learning, disease identification, Naïve Bayes, bacteria, fungus and virus

Abstract

As the population is increasing in India, there is a constant problem of food, its main reason is plant diseases. A lot of scientists are looking for new technology to solve this problem. One of the important techniques among them is machine learning. We can find plant diseases with the help of machine learning because machine learning by analysing the data (e.g., plant colour, leaf shape) finds the disease of the plant and also predicts it. With the help of this page, we can see how to solve plant diseases with the help of machine learning techniques.

References

Suthaharan S, Suthaharan S. Support vector machine. In: Machine learning models and algorithms for big data classification: thinking with examples for effective learning. Springer; 2016; 207–35.

Mokhtar U, Ali MA, Hassenian AE, Hefny H. Tomato leaves diseases detection approach based on support vector machines. In 2015 IEEE 11th International computer engineering conference (ICENCO). 2015 Dec 29; 246–250.

Chen P, An J, Shu S, Cheng R, Nie J, Jiang T, Wang ZL. Super‐durable, low‐wear, and high‐performance fur‐brush triboelectric nanogenerator for wind and water energy harvesting for smart agriculture. Adv Energy Mater. 2021 Mar; 11(9): 2003066.

Larose DT, Larose CD. K‐nearest neighbor algorithm. In: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley; 2004.

Shrivastava S, Singh SK, Hooda DS. Soybean plant foliar disease detection using image retrieval approaches. Multimed Tools Appl. 2017 Dec; 76(24): 26647–74.

Bhatia A, Chug A, Singh AP, Singh RP, Singh D. A Forecasting Technique for Powdery Mildew Disease Prediction in Tomato Plants. In Proceedings of Second Doctoral Symposium on Computational Intelligence: DoSCI 2021. Singapore: Springer; 2022; 509–520.

Bhatia A, Chug A, Singh AP, Singh RP, Singh D. A machine learning-based spray prediction model for tomato powdery mildew disease. Indian Phytopathol. 2022: 75(6): 225–230.

Pharm TN, Van Tran L, Dao SV. Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access. 2020 Oct 19; 8: 189960–73.

Zhang D, Wang Z, Jin N, Gu C, Chen Y, Huang Y. Evaluation of efficacy of fungicides for control of wheat fusarium head blight based on digital imaging. IEEE Access. 2020 Jun 11; 8: 109876–90.

Dhanasekaran S, Chigicherla DR, Tholeti SR, Pole GR. Intelligent System to Analyse Plant Diseases using Machine Learning Techniques. In 2022 IEEE International Conference on Applied Artificial Intelligence and Computing (ICAAIC). 2022 May 9; 150–154.

Xian TS, Ngadiran R. Plant diseases classification using machine learning. In J Phys: Conf Ser. 2021 Jul 1; 1962(1): 012024. IOP Publishing.

Kirti Rajpal N. Black rot disease detection in grape plant (Vitis vinifera) using colour based segmentation & machine learning. In 2020 IEEE 2nd international conference on advances in computing, communication control and networking (ICACCCN). 2020 Dec 18; 976–979.

Vijayalakshmi Murugan. An Effective Approach for Diagnosis of Plant Disease using ELM. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE). 2017; 4(12): 327–335. Available from: https://www.technoarete.org/common_abstract/ pdf/IJERCSE/v4/i12/Ext_63541.pdf

Chahal N. A Clustering Adaptive Neural Network Approach for Leaf Disease Identification. Int J Comput Appl. 2015 Jan 1; 120(11): 30–33.

Amoda N, Jadhav B, Naikwadi S. Detection and classification of plant diseases by image processing. Int J Innov Sci Eng Technol. 2014 Apr; 1(2): 70–4.

Published

2023-05-20

Issue

Section

Review Article