Document Type : Research Article
Authors
1 Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
2 Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract
Introduction
Pistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues is crucial for making decisions about topical garden management. Since it is not possible to diagnose psylla disease even after the onset of symptoms with the help of color images by drones, hyperspectral and multispectral sensors are needed. The main purpose of this study was to extract spectral bands suitable for distinguishing healthy leaves from psylla leaves. For this purpose, in this paper, a new method for selecting sensitive spectral properties from hyperspectral data with the high spectral resolution is presented. The intelligent selection of sensitive bands is a convenient way to build multispectral sensors for a specific application (in this article, the diagnosis of psylla leaves). Knowledge of disease-sensitive wavelengths can also help researchers analyze multispectral and hyperspectral aerial images captured by satellites or drones.
Materials and Methods
A total number of 160 healthy and diseased leaves were scanned in 64 spectral bands between 400-1100 nm with 10 nm spectral resolution. A random forest algorithm was used to identify the importance of features in classifying the dataset into diseased and healthy leaves. After computing the importance of the features, a clustering algorithm was developed to cluster the most important features into six clusters such that the center of clusters was 50 nm apart. To transfer the hyperspectral dataset into a multispectral dataset, the reflectance was averaged in spectral bands within ±15 nm of each cluster center and achieved six broad multispectral bands. Afterwards a support vector machine algorithm was utilized to classify the diseased and healthy leaves using both hyperspectral and multispectral datasets.
Results and Discussion
The center of clusters were 468 nm, 598 nm, 710 nm, 791 nm, 858 nm, and 1023 nm, which were calculated by taking the average of all the members assigned to the individual clusters. These are the most informative spectral bands to distinguish the pistachio leaves infected by Psylla from the healthy leaves. The F1-score was 90.91 when the hyperspectral dataset (all bands) was used, while the F1-score was 88.69 for the multispectral dataset. The subtle difference between the F1-scores indicates that the proposed pipeline in this study was able to select appropriately the sensitive bands while retaining all relevant information.
Conclusion
The importance of spectral bands in the visible and near-infrared region (between 400 and 1100 nm) was obtained to identify pistachio tree leaves infected with psylla disease. Based on the importance of spectral properties and using a clustering algorithm, six wavelengths were obtained as the best wavelengths for classifying healthy and diseased pistachio leaves. Then, by averaging the wavelengths at a distance of 15 nm from these six centers, the hyperspectral data (64 bands) became multispectral (6 bands). Since the correlation between the wavelengths in the near-infrared region was very high (more than 95%), out of the three selected wavelengths in the near-infrared region (710, 791, and 1023), only the 710-nm wavelength, which was closer to the visible region, was selected. The results of classification of infected and diseased leaves using hyperspectral and multispectral data showed that the degree of classification accuracy decreases by about 2% and if only 4 bands are used, the degree of accuracy decreases by about 3%.
The results of this study revealed that the proposed framework could be used for selecting the most informative spectral bands and accordingly develop custom-designed multispectral sensors for disease detection in pistachio. In addition, we could reduce the dimensionality of the hyperspectral datasets and avoid the issues related to the curse of dimensionalitylity.
Keywords
Open Access
©2020 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.
- Al-Saddik, H., J. C. Simon, and F. Cointault. 2018. Assessment of the Optimal Spectral Bands for Designing a Sensor for Vineyard Disease Detection: The Case of Flavescence Dorée. Precision Agriculture (0123456789). org/10.1007/s11119-018-9594-1.
- Alisaac, E., J. Behmann, M. T. Kuska, H.W. Dehne, and A. K. Mahlein. Hyperspectral Quantification of Wheat Resistance to Fusarium Head Blight: Comparison of Two Fusarium Species. European Journal of Plant Pathology. doi.org/10.1007/s10658-018-1505-9.
- Breiman, L. 2001. Random Forests. Machine Learning 45 (1): 5-32.
- Hastie, T., R. Tibshirani, and J. Friedman. 2009. Springer Series in Statistics The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag New York. springerlink.com/index/10.1007/b94608.
- Kheiralipour, , H. Ahmadi, A. Rajabipour, Sh. Rafiee, M. Javan-Nikkhah, D. S. Jayas, and K. Siliveru. 2016. Detection of Fungal Infection in Pistachio Kernel by Long-Wave near-Infrared Hyperspectral Imaging Technique. Quality Assurance and Safety of Crops and Foods 8 (1): 129-35. doi.org/10.3920/QAS2015.0606.
- Mahlein, A., Christian Oerke, U. Steiner, and H. Wilhelm Dehne. 2012. Recent Advances in Sensing Plant Diseases for Precision Crop Protection. European Journal of Plant Pathology 133 (1): 197-209.
- Moghimi, A., C. Yang, M. E. Miller, F. Shahryar Kianian, and P. M. Marchetto. A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Frontiers in Plant Science. doi.org/10.3389/fpls.2018.01182.
- Moghimi, A., M. H. Aghkhani, A. Sazgarnia, and M. H. Abbaspour-Fard. 2011. Improvement of NIR Transmission Mode for Internal Quality Assessment of Fruit Using Different Orientations. Journal of Food Process Engineering 34 (5): 1759-74.
- Moghimi, A., C. Yang, and J. A. Anderson. 2020. Aerial Hyperspectral Imagery and Deep Neural Networks for High-Throughput Yield Phenotyping in Wheat. Computers and Electronics in Agriculture 172: 105299. org/abs/1906.09666.
- Moghimi, A., C. Yang, J. A. Anderson, and S. K. Reynolds. 2019. Selecting Informative Spectral Bands Using Machine Learning Techniques to Detect Fusarium Head Blight in Wheat. In ASABE Annual International Meeting, Boston, MA. org/10.13031/aim.201900815. (August 13, 2019).
- Moghimi, A., C. Yang, and P. M. Marchetto. 2018. Ensemble Feature Selection for Plant Phenotyping: A Journey from Hyperspectral to Multispectral Imaging. IEEE Access 6: 56870-84.
- Mohammadigol, R., H. Khoshtaghaza, R. Malekfar, M. Mirabolfathi, and A. M. Nikbakht. 2013. Detection of Pistachio Aflatoxin Using Raman Spectroscopy and Artificial Neural Networks. Journal of Agricultural Machinery 5 (1): 1-9. (In Persian). http://dx.doi.org/10.22067/jam.v5i1.28122.
- Mutanga, O., and A. K. Skidmore. 2007. Red Edge Shift and Biochemical Content in Grass Canopies. ISPRS Journal of Photogrammetry and Remote Sensing 62 (1): 34-42.
- Nagasubramanian, , S. Jones, S. Sarkar, A. K. Singh, A. Singh, and G. Baskar. 2018.Hyperspectral Band Selection Using Genetic Algorithm and Support Vector Machines for Early Identification of Charcoal Rot Disease in Soybean Stems. Plant Methods 14 (1): 1-13. doi.org/10.1186/s13007-018-0349-9.
- Sankaran, S., A. Mishra, J. M. Maja, and R. Ehsani. 2011. Visible-near Infrared Spectroscopy for Detection of Huanglongbing in Citrus Orchards. Computers and Electronics in Agriculture 77 (2): 127-34. org/10.1016/j.compag.2011.03.004.
- Susic, N., U. Zibrat, S. Sirca, P. Strajnar, J. Razinger, M. Knapic, and B. Stare. 2018. Discrimination between Abiotic and Biotic Drought Stress in Tomatoes Using Hyperspectral Imaging. Sensors and Actuators, B: Chemical 273(June): 842-52.
- Stefan, T., J. Behmann, A. Steier, T. Kraska, O. Muller, U. Rascher, and A. K. Mahlein. Quantitative Assessment of Disease Severity and Rating of Barley Cultivars Based on Hyperspectral Imaging in a Non-Invasive, Automated Phenotyping Platform. Plant Methods 14 (1): 45. doi.org/10.1186/s13007-018-0313-8.
- Wahabzade, M., A. K. Mahlein, C. Bauckhage, U. Steiner, E. C. Oerke, and K. Kersting. Plant Phenotyping Using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants. Scientific Reports 6(February): 22482. http://www.nature.com/articles/srep22482.
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