Vol.65, No.1, 2020, pp.761-775, doi:10.32604/cmc.2020.010158
OPEN ACCESS
ARTICLE
Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine
  • Linguo Li1, 2, Lijuan Sun1, Jian Guo1, Shujing Li2, *, Ping Jiang3
1 School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.
2 College of Information Engineering, Fuyang Normal University, Fuyang, 236041, China.
3 Laboratory of Information and Computing Science, The Western University, Ontario, N6A 3K7, Canada.
* Corresponding Author: Shujing Li. Email: lsjing1981@163.com.
Received 14 February 2020; Accepted 03 June 2020; Issue published 23 July 2020
Abstract
As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and thresholds of the machine, and acquires the approximately optimal solution. And finally, a data set of crop diseases is used for verification, demonstrating that, compared with several other common machine learning methods, this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.
Keywords
Crops, disease identification, extreme learning machine, improved genetic algorithm.
Cite This Article
Li, L., Sun, L., Guo, J., Li, S., Jiang, P. (2020). Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine. CMC-Computers, Materials & Continua, 65(1), 761–775.
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