| تعداد نشریات | 127 |
| تعداد شمارهها | 7,196 |
| تعداد مقالات | 77,227 |
| تعداد مشاهده مقاله | 157,224,059 |
| تعداد دریافت فایل اصل مقاله | 118,406,883 |
تخمین مقدار آهن برگ سیب با استفاده از مدل مبتنی بر شبکه عصبی و پردازش تصویر | ||
| تحقیقات آب و خاک ایران | ||
| دوره 57، شماره 3، خرداد 1405، صفحه 751-766 اصل مقاله (1.4 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2026.397919.669970 | ||
| نویسندگان | ||
| حجت صادقی؛ ابراهیم سپهر* ؛ آیدین ایمانی | ||
| گروه علوم خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران | ||
| چکیده | ||
| کمبود آهن به عنوان یکی از شایعترین مشکلات تغذیهای گیاهان باغی واقع در خاکهای آهکی است. تشخیص سریع کمبود آهن با استفاده از پردازش تصویر و یادگیری ماشین میتواند به عنوان یک روش ارزان به رفع این مشکل کمک کند. از این رو برای بررسی ارتباط بین کمبود آهن و ویژگیهای رنگ برگ، یک پایگاه داده شامل 1575 تصویر برگ سیب با سطوح مختلف کمبود آهن (شدید، متوسط، کم و بدون کمبود) جمعآوری گردید. تصویربرداری با استفاده از دوربین گوشی هوشمند انجام گرفت و مقادیر آهن فعال و کل هر نمونه با دستگاه جذب اتمی اندازهگیری گردید. ویژگیهای رنگی از فضاهای رنگی RGB، Lab، HSV و NTSC به همراه ۸ شاخص رنگی ترکیبی استخراج شد. مدلسازی با دو رویکرد رگرسیون خطی و شبکه عصبی مصنوعی انجام گرفت. نتایج مدل خطی نشان داد مدل خطی قادر به پیشبینی آهن فعال با ضریب تعیین R2 = 0.74 است، اما با آهن کل همبستگی نشان نداد. مدل شبکه عصبی با دقت R2 = 0.80 و مقادیر خطای RMSE = 1.156 و MAPE=25.03 عملکرد بهتری نسبت به مدل خطی داشت. در نتیجه، مدل شبکه عصبی با استفاده از ویژگیهای رنگی برگ میتواند به عنوان روش سریع و غیر مخرب در تشخیص کمبود آهن و تخمین میزان آهن برگ سیب مورد استفاده قرار بگیرد. | ||
| کلیدواژهها | ||
| برگ سیب؛ یادگیری ماشین؛ آهن فعال؛ پردازش تصویر | ||
| عنوان مقاله [English] | ||
| Estimation of Iron Content in Apple Leaves Using an Artificial Neural Network and Image Processing Model | ||
| نویسندگان [English] | ||
| Hojjat Sadeghi؛ Ebrahim Sepehr؛ Aydin imani | ||
| Dept. of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran | ||
| چکیده [English] | ||
| Iron deficiency is one of the most common nutritional problems in fruit trees grown in calcareous soils. Rapid detection of iron deficiency using image processing and machine learning can serve as a low-cost method to address this issue. Therefore, to investigate the relationship between iron deficiency and leaf color characteristics, a database consisting of 1,500 apple leaf images with varying levels of iron deficiency (severe, moderate, mild, and none) was collected. Imaging was performed using a smartphone camera, and the active and total iron content of each sample was measured using an atomic absorption spectrometer. Color features were extracted from RGB, Lab, HSV, and NTSC color spaces, along with eight combined color indices. Modeling was performed using two approaches: linear regression and artificial neural networks. The linear model showed a moderate ability to predict active iron with a determination coefficient of R² = 0.74 but showed no correlation with total iron content. In contrast, the neural network model achieved better performance with R² = 0.80, RMSE = 1.156, and MAPE = 25.03. As a result, the ANN model based on leaf color features can be considered a rapid and non-destructive method for detecting iron deficiency and estimating iron content in apple leaves. | ||
| کلیدواژهها [English] | ||
| Apple leaf, Machine learning, Active iron, Image processing | ||
| مراجع | ||
|
Barbedo, J.G.A., 2013. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1), pp.1-12. Bai, G., Jenkins, S., Yuan, W., Graef, G.L., & Ge, Y. (2018). Field-based scoring of soybean iron deficiency chlorosis using RGB imaging and statistical learning. Frontiers in plant science, 9, 1002. https://doi.org/10.3389/fpls.2018.01002 Borhan, M.S., Panigrahi, S., Satter, M.A., & Gu, H. (2017). Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves. Information processing in agriculture, 4(4), 275-282. https://doi.org/10.1016/j.inpa.2017.07.005 Camargo, A., & Smith, J.S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems engineering, 102(1), 9-21. https://doi.org/10.1016/j.biosystemseng.2008.09.030 Chaney, R.L. (1984). Diagnostic practices to identify iron deficiency in higher plants. Journal of Plant Nutrition, 7(1-5), 47-67. https://doi.org/10.1080/01904168409363174 Chen, L.S., Zhang, S.J., Wang, K., Shen, Z.Q. and Deng, J.S., 2013. Identifying of rice phosphorus stress based on machine vision technology. Life Sci J, 10(2), pp.2655-2663. Culman, M.A., Gomez, J.A., Talavera, J., Quiroz, L.A., Tobon, L.E., Aranda, J.M., Garreta, L.E. and Bayona, C.J., 2017, April. A novel application for identification of nutrient deficiencies in oil palm using the internet of things. In 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) (pp. 169-172). IEEE. https://doi.org/10.1109/MobileCloud.2017.32 Firuzi, S., Sepehr, E., Imani, A., hossein pour, S. (2025). 'Development of an artificial neural network-based model for estimating the active iron content in grape leaves', Iranian Journal of Soil and Water Research, 55(11), pp. 2145-2156. doi: 10.22059/ijswr.2024.377062.669720 Ghosal, S., Blystone, D., Singh, A.K., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences, 115(18), 4613-4618. https://doi.org/10.1073/pnas.1716999115 Hajizadeh, N., Sepehr, E., Maleki, R. and Imani, A. (2024). Detection of iron deficiency in peaches using image processing and artificial neural network model. Iranian Journal of Soil and Water Research, 55(1), 69-81. doi: 10.22059/ijswr.2023.367213.669597 Hedley, C. 2015. The role of precision agriculture for improved nutrient management on farms.J.Sci. Food Agric.95, 12 – 19 .https://doi.org/10/f7krtc Jeyalakshmi, S., & Radha, R. (2017). A review on diagnosis of nutrient deficiency symptom in plant leaf image using digital iamge processing. ICTACT Journal on Image & Video Processing, 7(4). DOI: 10.21917/ijivp.2017.0216 Kendler, S., Aharoni, R., Young, S., Sela, H., Kis-Papo, T., Fahima, T. and Fishbain, B., 2022. Detection of crop diseases using enhanced variability imagery data and convolutional neural networks. Computers and Electronics in Agriculture, 193, p.106732. https://doi.org/10.1016/j.compag.2022.106732 Lee, K. J., & Lee, B. W. (2013). Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy, 48, 57-65. https://doi.org/10.1016/j.eja.2013.02.011 Leemans, V., Marlier, G., Destain, M.F., Dumont, B. and Mercatoris, B., 2017, April. Estimation of leaf nitrogen concentration on winter wheat by multispectral imaging. In Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017 (Vol. 10213, pp. 45-54). SPIE. Li, G., Kronzucker, H.J., & Shi, W. (2016). The response of the root apex in plant adaptation to iron heterogeneity in soil. Frontiers in Plant Science,7: 344. https://doi.org/10.3389/fpls.2016.00344 Liu, B., Zhang, Y., He, D. and Li, Y., 2017. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), p.11. Luz, P.H.D.C., Marin, M.A., Devechio, F.F.S., Romualdo, L.M., Zuniga, A.M.G., Oliveira, M.W.S., Bruno, O.M. (2018). Boron deficiency precisely identified on growth stage V4 of maize crop using texture image analysis. Communications in Soil Science and Plant Analysis, 49(2), 159-169. https://doi.org/10.1080/00103624.2017.1421644 Merry, R., Dobbels, A. A., Sadok, W., Naeve, S., Stupar, R. M., & Lorenz, A. J. (2022). Iron deficiency in soybean. Crop Science, 62 (1), 36-52. Mao, H., Gao, H., Zhang, X., Kumi, F., 2015. Nondestructive measurement of total ni-trogen in lettuce by integrating spectroscopy and computer vision. Scientia Horticulturae 184, 1–7. Mercado-Luna, A., Rico-García, E., Lara-Herrera, A., Soto-Zarazua, G., Ocampo-Velazquez, R., Guevara-Gonzalez, R., Torres-Pacheco, I. (2010). Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by color image analysis (RGB). African Journal of Biotechnology, 9(33). Morales, F., Grasa, R., Abadía, A., & Abadia, J. (1998). Iron chlorosis paradox in fruit trees. Journal of plant nutrition, 21(4), 815-825. Naik, M.R., Sivappagari, C.M.R. (2016). Plant leaf and disease detection by using HSV features and SVM classifier. International Journal of Engineering Science, 3794(260), 372À379. Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., Aparicio-Tejo, P., Lasa, B. (2009). New method to assess barley nitrogen nutrition status based on image colour analysis: comparison with SPAD-502. Computers and Electronics in Agriculture, 65(2), 213-218. Pestana, M., de Varennes, A., Abadia, J., & Faria, E.A. (2005). Differential tolerance to iron deficiency of citrus rootstocks grown in nutrient solution. Scientia Horticulturae, 104(1), 25-36. Rorie, R. L., Purcell, L. C., Karcher, D. E., & King, C. A. (2011). The assessment of leaf nitrogen in corn from digital images. Crop Science, 51(5), 2174-2180. Rout, G.R., & Sahoo, S. (2015). Role of iron in plant growth and metabolism. Reviews in Agricultural Science, 3, 1-24. Saberioon, M.M., Amin, M.S.M., Aimrun, W., Gholizadeh, A., & Anuar, A.A.R. (2013). Assessment of colour indices derived from conventional digital camera for determining nitrogen status in rice plants. Journal of Food, Agriculture & Environment, 11(2), 655-662. Sankaran, S., Mishra, A., Ehsani, R. and Davis, C., (2010). A review of advanced techniques for detecting plant diseases. Computers and electronics in agriculture, 72(1), pp.1-13. Sayeed, M.A., Shashikala, G., Pandey, S., Jain, R., & Kumar, S.N. (2016). Estimation of nitrogen in rice plant using image processing and artificial neural networks. Imperial Jurnal of Interdisciplinary research (IJIR), 2. Shi, P., Wang, Y., Xu, J., Zhao, Y., Yang, B., Yuan, Z., & Sun, Q. (2021). Rice nitrogen nutrition estimation with RGB images and machine learning methods. Computers and Electronics in Agriculture, 180, 105860. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience, 2016. Sun, Y., Gao, J., Wang, K., Shen, Z., & Chen, L. (2018). Utilization of machine vision to monitor the dynamic responses of rice leaf morphology and colour to nitrogen, phosphorus, and potassium deficiencies. Journal of Spectroscopy, 2018. Sulistyo, S.B., Wu, D., Woo, W.L., Dlay, S.S., Gao, B., (2018). Computational deep in-telligence vision sensing for nutrient content estimation in agricultural automation. IEEE Transactions on Automation Science and Engineering 15 (3), 1243–1257 Tewari, V. K., Arudra, A. K., Kumar, S. P., Pandey, V., & Chandel, N. S. (2013). Estimation of plant nitrogen content using digital image processing. Agricultural Engineering International: CIGR Journal, 15(2), 78-86. Vasconcelos, M.W., & Grusak, M.A. (2014). Morpho-physiological parameters affecting iron deficiency chlorosis in soybean (Glycine max L.). Plant and soil, 374, 161-172. Vakilian, K.A., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops. Computers and electronics in agriculture, 139, 153-163. Wang, Y., Hu, X., Hou, Z., Ning, J. and Zhang, Z., 2018. Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging. Journal of the Science of Food and Agriculture, 98(12), pp.4659-4664. Wang, F., Wang, K., Li, S., Gao, S., Xiao, C., Chen, B. & Diao, W. (2011). Estimation of canopy leaf nitrogen status using imaging spectrometer and digital camera in cotton. Acta Agronomica Sinica, 37(6), 1039-1048. Wang, Y., Wang, D., Zhang, G., & Wang, J. (2013). Estimating nitrogen status of rice using the image segmentation of GR thresholding method. Field Crops Research, 149, 33-39. Wang, Y., Wang, D., Shi, P., & Omasa, K. (2014). Estimating rice chlorophyll content and leaf nitrogen oncentration with a digital still color camera under natural light. Plant methods, 10, 1-11. Wiren, N.V., Grusak, M.A. (2000). Summary of IX international symposium of iron nutrition and interaction in plants. Journal of Plant Nutrition.23: 2083-2102 Wei, Y., Li, M., Sigrimis, N., 2010. Estimating Nitrogen Content of Cucumber Leaves Based on NIR Spectroscopy. Sensor Letters 8 (1), 145–150. Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38(1), 259-269. Xu, G., Zhang, F., Shah, S.G., Ye, Y., & Mao, H. (2011). Use of leaf color images to identify Yuzhu, H., Xiaomei, W., & Shuyao, S. (2011). Nitrogen determination in pepper (Capsicum frutescens L.) plants by color image analysis (RGB). African Journal of Biotechnology, 10(77), 17737-17741. Yuan, Y., Chen, L., Li, M., Wu, N., Wan, L., Wang, S., 2016. Diagnosis of nitrogen nutrition of rice based on image processing of visible light. Proc. IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications 228–232. | ||
|
آمار تعداد مشاهده مقاله: 82 تعداد دریافت فایل اصل مقاله: 63 |
||