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International Journal of Food Properties Volume 22, 2019 - Issue 1

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Original Article

Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder

Yingshu Penga College of Forestry, Nanjing Forestry University, Nanjing, PR ChinaCorrespondence[email protected]
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Yi Wangb Jiangsu Wiscom Technology Co. Ltd, Nanjing, PR ChinaView further author information

Pages 1720-1732 | Received 16 May 2019, Accepted 27 Sep 2019, Published online: 10 Oct 2019

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https://doi.org/10.1080/10942912.2019.1675692
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Citations (4)

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Articles from other publishers (4)

Hengkai Li, Beibei Zhou, Feng Xu & Zhian Wei. (2022) Hyperspectral characterization and chlorophyll content inversion of reclaimed vegetation in rare earth mines. Environmental Science and Pollution Research 29:24, pages 36839-36853.
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Retno Damayanti, Nurul Rachma, Dimas Firmanda Al Riza & Yusuf Hendrawan. (2021) The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network. Pertanika Journal of Science and Technology 29:4.
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Yuxing Fan, Yingying Xiong, Yingying Zhang, Zhangsong Jiang, Haihui Tang, Ligui Wu, Mi Li, Xiaoyu Xiao, Cui Hu & Xiaoming Zou. (2021) Method to characterize color of biochar and its prediction with biochar yield as model property. Biochar.
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Utpal Barman, Animesh Sarmah, Diganto Sahu & Golap Gunjan Barman. 2021. Proceedings of the International Conference on Computing and Communication Systems. Proceedings of the International Conference on Computing and Communication Systems 287 295 .

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