Linguo Li1, 2, Lijuan Sun1, Jian Guo1, Shujing Li2, *, Ping Jiang3
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158
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… More >
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