Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation

Sulistyo, Susanto, Wu, Di, Woo, Wai Lok, Dlay, Satnam and Gao, Bin (2018) Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation. IEEE Transactions on Automation Science and Engineering, 15 (3). pp. 1243-1257. ISSN 1545-5955

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This paper presents a novel computational intelligence vision sensing approach to estimate nutrient content in wheat leaves by analyzing color features of the leaves images captured on field with various lighting conditions. We propose the development of deep sparse extreme learning machines (DSELM) fusion and genetic algorithm (GA) to normalize plant images as well as to reduce color variability due to a variation of sunlight intensities. We also apply the DSELM in image segmentation to differentiate wheat leaves from a complex background. In this paper, four moments of color distribution of the leaves images (mean, variance, skewness, and kurtosis) are extracted and utilized as predictors in the nutrient estimation. We combine a number of DSELMs with committee machine and optimize them using the GA to estimate nitrogen content in wheat leaves. The results have shown the superiority of the proposed method in the term of quality and processing speed in all steps, i.e., color normalization, image segmentation, and nutrient prediction, as compared with other existing methods.

Item Type: Article
Uncontrolled Keywords: Agricultural automation, committee machines, computational intelligence, deep learning, deep neural networks, image processing
Subjects: D400 Agriculture
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 26 Mar 2019 10:08
Last Modified: 10 Oct 2019 20:34

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