A review of neural networks in plant disease detection using hyperspectral data
| Autor: | Ganesan Vadamalai, Siva K. Balasundram, Kamlesh Golhani, Biswajeet Pradhan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Disease detection Artificial neural network Computer science business.industry Early disease Hyperspectral imaging Forestry Pattern recognition 02 engineering and technology Spectral bands Aquatic Science 01 natural sciences Plant disease Computer Science Applications 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Animal Science and Zoology Artificial intelligence business Agronomy and Crop Science 010606 plant biology & botany |
| Zdroj: | Information Processing in Agriculture. 5:354-371 |
| ISSN: | 2214-3173 |
| Popis: | © 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data. |
| Databáze: | OpenAIRE |
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