@Article{cmc.2020.010158,
AUTHOR = {Linguo Li, Lijuan Sun, Jian Guo, Shujing Li, Ping Jiang},
TITLE = {Identification of Crop Diseases Based on Improved Genetic  Algorithm and Extreme Learning Machine},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {761--775},
URL = {http://www.techscience.com/cmc/v65n1/39593},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010158}
}