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

References

• Arregui, L. M.; Lasa, B.; Lafarga, A.; Iraneta, I.; Baroja, E.; Quemada, M. Evaluation of Chlorophyll Meters as Tools for N Fertilization in Winter Wheat under Humid Mediterranean Conditions. Eur. J. Agron. 2006, 24, 140–148. DOI: 10.1016/j.eja.2005.05.005.
   Web of Science ®Google Scholar
• Pagola, M.; Irigoyen, I.; Bustince, H.; Barrenechea, E.; Aparicio-Tejo, P.; Lamsfus, C.; Lasa, B. New Method to Assess Barley Nitrogen Nutrition Status Based on Image Colour Analysis. Comput. Electr. Agric. 2009, 65, 213–218. DOI: 10.1016/j.compag.2008.10.003.
   Web of Science ®Google Scholar
• Vibhute, A.; Bodhe, S. K. Color Image Processing Approach for Nitrogen Estimation of Vineyard. Inter. J. Agric. Sci. Res. 2013, 3, 189–196. DOI: 10.1016/S0007-0785(94)80025-1.
   Google Scholar
• Pierce, F. J.; Nowak, P. Aspects of Precision Agriculture. Adv. Agron. 1999, 67, 1–85. DOI: 10.1016/S0065-2113(08)60513-1.
   Web of Science ®Google Scholar
• Amaral, E. S. D.; Silva, D. V.; Anjos, L. D.; Schilling, A. C.; Andrea-Carla, D.; Mielke, M. S. Relationships between Reflectance and Absorbance Chlorophyll Indices with Rgb (red, Green, Blue) Image Components in Seedlings of Tropical Tree Species at Nursery Stage. New For. 2018, 1–12. DOI: 10.1007/s11056-018-9662-4.
   Google Scholar
• Noh, H.; Zhang, Q.; Shin, B.; Han, S.; Feng, L. A Neural Network Model of Maize Crop Nitrogen Stress Assessment for A Multi-spectral Imaging Sensor. Biosyst. Eng. 2006, 94, 477–485. DOI: 10.1016/j.biosystemseng.2006.04.009.
   Web of Science ®Google Scholar
• Su, C. H.; Fu, C. C.; Chang, Y. C.; Nair, G. R.; Ye, J. L.; Chu, I. M.; Wu, W. T. Simultaneous Estimation of Chlorophyll a and Lipid Contents in Microalgae by Three-color Analysis. Biotech. Bioengin. 2010, 99, 1034–1039. DOI: 10.1002/bit.21623.
   Web of Science ®Google Scholar
• Liu, P.; Shi, R.; Zhang, C.; Zeng, Y.; Wang, J.; Tao, Z.; Gao, W. Integrating Multiple Vegetation Indices via an Artificial Neural Network Model for Estimating the Leaf Chlorophyll Content of Spartina Alterniflora under Interspecies Competition. Environ. Monitor. Assess. 2017, 189, 596. DOI: 10.1007/s10661-017-6323-6.
   PubMed Web of Science ®Google Scholar
• Gaviria-Palacio, D.; Guaqueta-Restrepo, J. J.; Pineda-Tobon, D. M.; Perez, J. C. Fast Estimation of Chlorophyll Content on Plant Leaves Using the Light Sensor of a Smartphone. Dyna. 2017, 84, 234–239.
   Google Scholar
• Gupta, S. D.; Pattanayak, A. K. Intelligent Image Analysis (iia) Using Artificial Neural Network (ann) for Non-invasive Estimation of Chlorophyll Content in Micropropagated Plants of Potato. In Vitro Cell.r Develop. Bio. Plant. 2017, 14, 1–7. DOI: 10.1007/s11627-017-9825-6.
   Google Scholar
• Sulistyo, S. B.; Wu, D.; Woo, W. L.; Dlay, S. S.; Gao, B. Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation. IEEE Trans. Autom. Sci. Eng. 2017, 15(3), 1243–1257. DOI: 10.1109/TASE.2017.2770170.
   Web of Science ®Google Scholar
• Yadav, S. P.; Ibaraki, Y.; Gupta, S. D. Estimation of the Chlorophyll Content of Micropropagated Potato Plants Using Rgb Based Image Analysis. Plant Cell Tissue Organ. Cul. 2010, 100, 183–188. DOI: 10.1007/s11240-009-9635-6.
   Web of Science ®Google Scholar
• Gago, J.; Martinez-Nunez, L.; Landin, M.; Gallego, P. P. Artificial Neural Networks as an Alternative to the Traditional Statistical Methodology in Plant Research. J. Plant Physiol. 2010, 167, 23–27. DOI: 10.4161/psb.5.6.11702.
   PubMed Web of Science ®Google Scholar
• Vesali, F.; Omid, M.; Kaleita, A.; Mobli, H. Development of an Android App to Estimate Chlorophyll Content of Corn Leaves Based on Contact Imaging. Comput. Electron. Agric. 2015, 116, 211–220. DOI: 10.1016/j.compag.2015.06.012.
   Web of Science ®Google Scholar
• Zhang, M.; Gunturk, B. K. Multiresolution Bilateral Filtering for Image Denoising. IEEE Trans. Image Process 2008, 17, 2324–2333. DOI: 10.1109/TIP.2008.2006658.
   PubMed Web of Science ®Google Scholar
• Otsu, N. A Threshold Selection Method from Gray-level Histograms. IEEE Trans. Syst. Man Cybern. 2007, 9, 62–66. DOI: 10.1109/TSMC.1979.4310076.
   Web of Science ®Google Scholar
• Gonzalez, R. C.; Woods, R. E. Digital Image Processing, 3rd ed. Prentice-Hall: Upper Saddle River, USA, 2008; pp. 636. ISBN 978-0131687288. DOI:10.1109/IEMDC.2013.6556306.
   Google Scholar
• Huang, T.; Yang, G.; Tang, G. A Fast Two-dimensional Median Filtering Algorithm. IEEE Trans. Acoustic. Speech. Sign. Process. 1979, 27, 13–18. DOI: 10.1109/TASSP.1979.1163188.
   Google Scholar
• Yuan, Z.; Cao, Q.; Zhang, K. Syed Tahir Ata-Ul-Karim, Yongchao Tian, Yan Zhu, Weixing Cao, and Xiaojun Liu. Optimal Leaf Positions for SPAD Meter Measurement in Rice. Front. Plant Sci. 2016, 7, 719.
   PubMed Web of Science ®Google Scholar
• Fischer, P.; HFfgen, K. U. Computing a Maximum Axis-aligned Rectangle in a Convex Polygon. Inf. Process. Lett. 1994, 51, 189–193. DOI: 10.1016/0020-0190(94)00079-4.
   Web of Science ®Google Scholar
• Lecun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature. 2015, 521, 436–449. DOI: 10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Miotto, R.; Wang, F.; Wang, S.; Jiang, X.; Dudley, J. T. Deep Learning for Healthcare: Review, Opportunities and Challenges. Briefings Bioinf. 2017, 1–11. DOI: 10.1093/bib/bbx044.
   PubMed Web of Science ®Google Scholar
• Wright, J.; Ma, Y.; Mairal, J.; Sapiro, G.; Huang, T. S.; Yan, S. Sparse Representation for Computer Vision and Pattern Recognition. Proc. IEEE. 2010, 98, 1031–1044. DOI: 10.1109/jproc.2010.2044470.
   Web of Science ®Google Scholar
• Sun, W.; Shao, S.; Zhao, R.; Yan, R.; Zhang, X.; Chen, X. A Sparse Auto-encoder-based Deep Neural Network Approach for Induction Motor Faults Classification. Measurement. 2016, 89, 171–178. DOI: 10.1016/j.measurement.2016.04.007.
   Web of Science ®Google Scholar
• Bourlard, H.; Kamp, Y. Auto-association by Multilayer Perceptrons and Singular Value Decomposition. Biol. Cyber. 1988, 59, 291–294. DOI: 10.1007/bf00332918.
   PubMed Web of Science ®Google Scholar
• Kullback, S.; Leibler, R. A. On Information and Sufficiency. Ann. Math. Stats. 1951, 22, 79–86. DOI: 10.1214/aoms/1177729694.
   Google Scholar
• Balamurugan, P.; Shevade, S.; Babu, T. R. Scalable Sequential Alternating Proximal Methods for Sparse Structural Svms and Crfs. Know. Inf. Syst. 2014, 38(3), 599–621. DOI: 10.1007/s10115-013-0681-3.
   Web of Science ®Google Scholar
• Hong, W. C. Electric Load Forecasting by Seasonal Recurrent Svr (support Vector Regression) with Chaotic Artificial Bee Colony Algorithm. Energy. 2011, 6, 5568–5578. DOI: 10.1016/j.energy.2011.07.015.
   Google Scholar
• Ibarra-Berastegi, G.; Saenz, J.; Esnaola, G.; Ezcurra, A.; Ulazia, A. Short-term Forecasting of the Wave Energy Flux: Analogues, Random Forests, and Physics-based Models. Ocean Eng. 2015, 104, 530–539. DOI: 10.1016/j.oceaneng.2015.05.038.
   Web of Science ®Google Scholar
• Xu, J.; Xiang, L.; Liu, Q.; Gilmore, H.; Wu, J.; Tang, J.; Madabhushi, A. Stacked Sparse Autoencoder (ssae) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE Trans. Med. Imaging. 2016, 35, 119. DOI: 10.1109/ISBI.2014.6868041.
   PubMed Web of Science ®Google Scholar
• Asraf, M. H.; Dalila, N. K.; Faiz, A. Z.; Aminah, S. N.; Nooritawati, M. T. A Fuzzy Inference System for Diagnosing Oil Palm Nutritional Deficiency Symptoms. ARPN J. Engg. Appl. Sci. 2017, 12(10), 3244–3250.
   Google Scholar