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Detection of plant leaf nutrients using convolutional neural network based Internet of Things data acquisition | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 1175-1186 اصل مقاله (569.01 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5194 | ||
| نویسندگان | ||
| R Sathyavani* 1؛ K JaganMohan2؛ B Kalaavathi3 | ||
| 1Department of IT, Institute Of Road and Transport Technology, Erode, India. | ||
| 2Department of IT, Annamalai University, Chidambaram, India. | ||
| 3Department of CSE, KSR Institute for Engineering and Technology, Tiruchengode, India. | ||
| تاریخ دریافت: 22 اسفند 1399، تاریخ بازنگری: 04 خرداد 1400، تاریخ پذیرش: 30 خرداد 1400 | ||
| چکیده | ||
| In this paper, the study detects the nutritional deficiencies from these leaves using Internet of Thing (IoT) based image acquisition and nutrition analyser devices. The former captures the color of the leaf and the latter helps in finding the nutrients in each zone based on the image captured by the device. The study uses an improved convolutional neural network to detect automatically the nutrients present in a leaf. The type of leaf is considered from the plants including coriander, tomato, pepper, chili, etc. The Convolutional Neural Network (CNN) is used to extract the patterns of leaf images from the data capturing IoT devices and nutrition analyser device. The system stores and process the data in cloud, where the CNN integrated in Virtual Machines enables the process of input data and process it and sends the report to the authority. A total of 3000 images are collected out of various disorders in five different plants. A 5 fold cross-validation is conducted on training and testing dataset. The system is tested in terms of accuracy, sensitivity, specificity, f-measure, geometric mean and percentage error. The comparison made with existing models shows an improved detection accuracy by CNN than other deep learning models. | ||
| کلیدواژهها | ||
| Plant Leaf Nutrients؛ Detection؛ Convolutional Neural Network؛ Internet of Things | ||
| مراجع | ||
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[1] A. Aballa, H. Cen, L. Wan, K. Mehmood and Y. He, Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-enabled Dynamic Model. IEEE Transactions on Industrial Informatics, (2020). [2] M. Bahram, T. Netherway, F. Hildebrand, K. Pritsch, R.Drenkhan, K. Loit and L. Tedersoo, Plant nutrientacquisition strategies drive topsoil microbiome structure and function. New Phytologist, 227(4), (2020) 1189-1199. [3] N. S. Binti Kassim, Nutrient Deficiency Detection in Maize (Zea mays L.) leaves using Image Processing (Doctoral dissertation, Universiti Teknologi MARA), (2020). [4] M. Broadley, P. Brown, I. Cakmak, Z. Rengel and F. Zhao, Function of nutrients: micronutrients. In Marschner’s mineral nutrition of higher plants, Academic Press, (2012) 191-248. [5] H. J. Butler, S. Adams, M. R. McAinsh and F. L. Martin, Detecting nutrient deficiency in plant systems using synchrotron Fourier-transform infrared microspectroscopy. Vibrational Spectroscopy, 90, (2017) 46-55. [6] W. J. Cho, H. J. Kim, D. H. Jung, D. W. Kim, T. I. Ahn, and J. E. Son, On-site ion monitoring system for precision hydroponic nutrient management. Computers and electronics in agriculture, 146, (2018) 51-58. [7] A. K. Dubey, R. Ratan and A. Rocha, Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency. Cluster Computing, (2019) 1-10. [8] S. Dudala, S. K. Dubey and S. Goel, Microfluidic soil nutrient detection system: integrating nitrite, pH, and electrical conductivity detection. IEEE Sensors Journal, 20(8), (2020) 4504-4511. [9] N. K. Fageria, V. C. Baligar and R. B. Clark, Micronutrients in crop production. Advances in agronomy, 77, (2002) 185-268. [10] P. Fernando and L. Lacatan, Microcontroller-Based Soil Nutrients Analyzer for Plant Applicability using Adaptive Neuro-Fuzzy Inference System. no, 5576, (2020) 5576-5581. [11] E. F. George, M. A. Hall and G. J. De Klerk, The components of plant tissue culture media I: macro-and micro nutrients. In Plant propagation by tissue culture Springer, Dordrecht, (2008) 65-113. [12] D. H. Jung, H. J. Kim, G. L. Choi, T. I. Ahn, J. E. Son and K. A. Sudduth, Automated lettuce nutrient solution management using an array of ion-selective electrodes. Transactions of the ASABE, 58(5), (2015) 1309-1319. [13] H. J. Kim, W. K. Kim, M. Y. Roh, C. I. Kang,, J. M. Park and K. A. Sudduth, Automated sensing of hydroponic macronutrients using a computer-controlled system with an array of ion-selective electrodes. Computers and electronics in agriculture, 93, (2013) 46-54. [14] F. J. Maathuis, Physiological functions of mineral macronutrients. Current opinion in plant biology, 12(3), (2009) 250-258. [15] A. McCauley, C. Jones and J. Jacobsen, Plant nutrient functions and deficiency and toxicity symptoms. Nutrient management module, 9, (2009) 1-16. [16] A. Noori, A. Ngo, P. Gutierrez, S. Theberge and J. C. White, Silver nanoparticle detection and accumulation in tomato (Lycopersicon esculentum). Journal of Nanoparticle Research, 22, (2020) 1-16. [17] L. Schwendenmann and B. Michalzik, Impact of Phytophthora agathidicida infection on canopy and forest floor plant nutrient concentrations and fluxes in a kauri-dominated forest. Ecology and evolution, 11(9), (2021) 4310-4324. [18] A. Shah, P. Gupta and Y. M. Ajgar, Macro-Nutrient Deficiency Identification in Plants Using Image Processing and Machine Learning. In 2018 3rd International Conference for Convergence in Technology (I2CT) (2018, April) 1-4. [19] S. Sivagami and S. Mohanapriya, Automatic detection of tomato leaf deficiency and its result of disease occurrence through image processing. Int. J. Innov. Technol. Explor. Eng, 8(11), (2019) 4165-4172. [20] C. Tedore and D. E. Nilsson, Ultraviolet vision aids the detection of nutrient-dense non-signaling plant foods. Vision Research, 183, (2021) 16-29. [21] S. Trilles, J. Torres-Sospedra, O. Belmonte, F. J. Zarazaga-Soria, A., Gonz´alez-P´erez and J. Huerta, Sustainable ´ Computing: Informatics and Systems, (2019). [22] M. Vassallo-Barco, L. Vives-Garnique, V. Tuesta-Monteza, H. I. Mej´ıa-Cabrera and R. Y. Toledo, Automatic Detection of Nutritional Deficiencies In Coffee Tree Leaves Through Shape And Texture Descriptors. Journal of Digital Information Management, 15(1), (2017). [23] M. Vassallo-Barco, L. Vives-Garnique, V. Tuesta-Monteza, H. I. Mej´ıa-Cabrera and R. Y. Toledo, Automatic Detection of Nutritional Deficiencies In Coffee Tree Leaves Through Shape And Texture Descriptors. Journal of Digital Information Management, 15(1), (2017). [24] P. Yang, X. Liv and Z. Nie, Determination of the nutrient profile in plant materials using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid models. Optics Express, 28(15), (2020) 23037-23047. [25] J. A. Zettler, A. Collier, B. Leidersdorf and M. P. Sanou, Plants in your ants: using ant mounds to test basic ecological principles. The american biology Teacher, 72(3), (2010) 172-175. | ||
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