Abstract
Mulch film is usually mixed in with cotton during machine-harvesting and processing, which reduces the cotton quality. This paper presents a novel sorting algorithm for the online detection of film on cotton using hyperspectral imaging with a spectral region of 1000 - 2500 nm. The sorting algorithm consists of a group of stacked autoencoders, two optimization modules and an extreme learning machine (ELM) classifier. The variable-weighted stacked autoencoders (VW-SAE) are constructed to extract the features from hyperspectral images, and an artificial neural network (ANN), which is one optimization module, is applied to optimize the parameters of the VW-SAE. Then, the extracted features are input in the ELM to classify four types of objects: background, film on background, cotton and film on cotton. The ELM is optimized by a new optimizer (grey wolf optimizer), which can adjust the hidden nodes and parameters of the ELM simultaneously. A group of experiments was carried out to evaluate the performance of the proposed sorting algorithm using cotton that was provided by a Xinjiang municipality cotton ginning company. The experimental results show that the VW-SAE can improve the classification accuracies by approximately 15%. The overall recognition rate of the proposed algorithm is over 95%, and its recognition time is comparable to some state-of-the-art methods.
Highlights
Cotton is one of the most important crops in the world
The extreme learning machine (ELM) adopted a single hidden layer neural network, and the activation function was the sigmoid in order to conduct nonlinear classification
The weights, biases and number of hidden nodes of the ELM were simultaneously optimized by the grey wolf optimizer (GWO) algorithm
Summary
Cotton is one of the most important crops in the world. As the main cotton-producing province in China, Xinjiang has widely applied mulch film covering technology to retain the soil moisture, to maintain the soil structure and to prevent pests [1]. Mulch film is often mixed with cotton during the machine-harvesting and machine-processing steps, which results in reduced cotton quality. Some techniques have been developed for detecting foreign matter in cotton, such as electrostatic separation, ultrasonic detection and computer vision detection [2], [3]. Electrostatic separation is a rudimentary method that utilizes the charge characteristics to distinguish the film from cotton. It is affected by many uncertainties, such as voltage, and it results in
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