Abstract
Vine nutrition is a key element of vineyard management. Nutrient disorders affect vine growth, crop yield, berry composition, and wine quality. Each vineyard may have a unique combination of soil type, vine age, canopy architecture, cultivar and rootstock. Therefore nutritional requirements vary between vineyards and even locations within a vineyard. Nutritional disorders can be detected visually on leaves, fruits, stems or roots. The advancement of image processing and machine learning has made it feasible to develop rapid tools to assess vine nutritional disorders using these symptoms. This paper presents our proposed method of using a smartphone app to capture and analyse images of vine leaves for identifying nutritional disorders of grapevines rapidly and conveniently. Nutrient deficiency/toxicity symptoms were created in hydroponically grown grapevines of both red and white varieties. RGB (red, green, and blue) images of old and young leaves were taken weekly to track the progression of symptoms. A benchmarked dataset was developed through a laboratory based nutrient analysis of the petioles. A wide range of features (e.g., texture, smoothness, contrast and shape) were selected for the following customised machine learning techniques. Our proposed algorithm was developed to identify specific deficiency and toxicity symptoms through training and testing process. The support vector machine has achieved a 98.99% average accuracy in the testing.
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Agrios, G.N.: Plant Pathology. Elsevier Academic Press, Amsterdam (2005)
Taiz, L., Zeiger, E.: Plant Physiology, vol. 4, pp. 67–86. Sinauer Associates, Sunderland (2006). https://doi.org/10.1109/icacea.2015.7194375
Bock, C.H., Parker, P.E., Cook, A.Z., Gottwald, T.R.: Characteristics of the perception of different severity measures of citrus canker and the relationships between the various symptom types. Plant Dis. 92, 927–939 (2008). https://doi.org/10.1094/pdis-92-6-0927
Kaur, S., Pandey, S., Goel, S.: Plants disease identification and classification through leaf images: a survey. Arch. Comput. Methods Eng. 26, 507–530 (2019). https://doi.org/10.1007/s11831-018-9255-6
Brady, N.C., Weil, R.R.: Instructor’s manual with test item file to accompany The Nature and Properties of Soils, Fourteenth Edition (2008)
Fageria, N.K.: Maximizing Crop Yields. Marcel Dekker, New York (1992). https://doi.org/10.1109/CIS2018.2018.00044
Fageria, N.K., Filho, M.P.B., Moreira, A., Guimarães, C.M.: Foliar fertilization of crop plants. J. Plant Nutr. 32, 1044–1064 (2009). https://doi.org/10.1080/01904160902872826
Landon, J.R.: A Handbook for Soil Survey and Agricultural Land Evaluation in the Tropics and Subtropics. Taylor & Francis, London (2014)
Nagabhushan, T.N., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., Chaydevi, M.L. (eds.): CCIP 2017. CCIS, vol. 801. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-9059-2
Bhange, M., Hingoliwala, H.A.: Smart farming: pomegranate disease detection using image processing. Procedia Comput. Sci. 58, 280–288 (2015). https://doi.org/10.1016/j.procs.2015.08.022
Shi, Y., Huang, W., Luo, J., Huang, L., Zhou, X.: Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Comput. Electron. Agric. 141, 171–180 (2017). https://doi.org/10.1016/j.compag.2017.07.019
Huang, W., et al.: New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE J. Sel. Top Appl. Earth Obs. Remote Sens. 7, 2516–2524 (2014). https://doi.org/10.1109/JSTARS.2013.2294961
Jhuria, M., Kumar, A., Borse, R.: Image processing for smart farming: detection of disease and fruit grading. In: IEEE 2nd International Conference on Image Information Process, ICIIP 2013, pp. 521–526. IEEE (2013). https://doi.org/10.1109/ICIIP.2013.6707647
Husin, Z., Bin Md Shakaff, A.Y., Bin Abdul Aziz, A.H., Bin Mohamed Farook, R.B.S.: Feasibility study on plant chili disease detection using image processing techniques. In: Proceedings of the 3rd International Conference on Intelligent Systems Modelling and Simulation, ISMS 2012, pp. 291–296 (2012) https://doi.org/10.1109/ISMS.2012.33
Badnakhe, M.R.: Infected leaf analysis and comparison by Otsu threshold and k-means clustering. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2, 449–452 (2012)
Al Hiary, H., Bani Ahmad, S., Reyalat, M., Braik, M., ALRahamneh, Z.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17, 31–38 (2011)
Zhang, C., Wang, X., Li, X.: Design of monitoring and control plant disease system based on DSP&FPGA. In: 2nd International Conference on Networks Security, Wireless Communications and Trusted Computing, NSWCTC 2010, vol. 2, pp. 479–482 (2010). https://doi.org/10.1109/NSWCTC.2010.246
Phadikar, S., Sil, J.: Rice disease identification using pattern recognition techniques. In: Proceedings of the 11th International Conference on Computer and Information Technology, ICCIT 2008, pp. 420–423 (2008). https://doi.org/10.1109/ICCITECHN.2008.4803079
Kiaee, N., Hashemizadeh, E., Zarrinpanjeh, N.: Using GLCM features in Haar wavelet transformed space for moving object classification. IET Intell. Transp. Syst. 13, 1148–1153 (2019). https://doi.org/10.1049/iet-its.2018.5192
Sun, W., Zeng, N., He, Y.: Morphological Arrhythmia automated diagnosis method using gray-level co-occurrence matrix enhanced convolutional neural network. IEEE Access 7, 67123–67129 (2019). https://doi.org/10.1109/ACCESS.2019.2918361
Shoumy, N.J., Ang, L.-M., Motiur Rahaman, D.M.: Multimodal big data affective analytics. In: Seng, K.P., Ang, L.-M., Liew, A.W.-C., Gao, J. (eds.) Multimodal Analytics for Next-Generation Big Data Technologies and Applications, pp. 45–71. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-97598-6_3
Paul, M., Musfequs Salehin, M.: Spatial and motion saliency prediction method using eye tracker data for video summarization. IEEE Trans. Circ. Syst. Video Technol. 29, 1856–1867 (2019). https://doi.org/10.1109/TCSVT.2018.2844780
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Rahaman, D.M.M. et al. (2019). Grapevine Nutritional Disorder Detection Using Image Processing. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_15
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