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تشخیص کمبود آهن در هلو با استفاده از پردازش تصویر و مدل شبکه عصبی مصنوعی | ||
| تحقیقات آب و خاک ایران | ||
| دوره 55، شماره 1، فروردین 1403، صفحه 69-81 اصل مقاله (1.71 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2023.367213.669597 | ||
| نویسندگان | ||
| نسیم حاجی زاده1؛ ابراهیم سپهر* 1؛ رامین ملکی2؛ آیدین ایمانی1 | ||
| 1گروه علوم خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران | ||
| 2گروه پژوهشی شیمی تجزیه، جهاد دانشگاهی آذربایجان غربی، ارومیه، ایران | ||
| چکیده | ||
| پایش سریع و دقیق شرایط تغذیه‌ای باغ‌های میوه برای توصیه بهینه کودی یک بخش حیاتی در بهبود عملکرد و افزایش کیفیت محصولات کشاورزی است. روش‌های آزمایشگاهی فعلی مورد استفاده برای وضعیت تغذیه درختان میوه گران، دشوار، زمان‌بر و نیازمند فرد متخصص هستند. این تحقیق به منظور تعیین میزان کمبود آهن در درختان هلو، روش پردازش تصویر و مدل شبکه عصبی استفاده شد. یک پایگاه داده شامل 800 تصویر از نمونه‌های برگ هلو در ابتدا تهیه و تصاویر با استفاده از روش خوشه‌بندی KNN در چهار کلاس بدون کمبود، کمبود کم، کمبود متوسط و کمبود شدید طبقه‌بندی شدند. عملیات پیش‌پردازش، استخراج ویژگی‌ها و مدل‌سازی با استفاده از شبکه عصبی در نرم‌افزار متلب نسخه 2017 انجام گرفت. ویژگی‌های میانگین و انحراف معیار از مولفه‌های فضاهای رنگی RGB، HSV و Lab هر تصویر استخراج شدند و سپس الگوریتم آنالیز مولفه اصلی (PCA) بر روی بردار ویژگی اعمال شد. برای تعیین ساختار بهینه شبکه معیارهای دقت، صحت، بازیابی و معیار F برای تعیین تعداد ورودی‌های بهینه و تعداد نورون‌های متناظر با هر ترکیب ویژگی‌های ورودی (PCها) استفاده شد. نتایج نشان داد که مدل شبکه عصبی با ساختار 4 – 36 – 6 قادر است با دقت (54/0 ± 73/89 %)، صحت (57/0 ± 59/89 %)، بازیابی (51/0 ± 52/89 %) و معیار F (54/0 ± 55/89 %) میزان سطح کمبود آهن در برگ درخت هلو را تشخیص دهد. نتایج بدست آمده از ماتریس اغتشاش و مدل توسعه داده شده نشان داد که این روش قادر است با کارایی بالا شدت کمبود آهن در برگ درختان هلو را تشخیص دهد. | ||
| کلیدواژه‌ها | ||
| هلو؛ کمبود آهن؛ پردازش تصویر؛ شبکه عصبی؛ خوشه‌بندی KNN | ||
| عنوان مقاله [English] | ||
| Detection of iron deficiency in peaches using image processing and artificial neural network model | ||
| نویسندگان [English] | ||
| Nasim Hajizadeh1؛ Ebrahim Sepehr1؛ Ramin Maleki2؛ Aydin Imani1 | ||
| 1Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran | ||
| 2Department of Analytical Chemistry, Academic Center for Education, Culture and Research of West Azerbaijan, Urmia, Iran. | ||
| چکیده [English] | ||
| Accurately and promptly monitoring the nutritional conditions of fruit orchards is crucial for providing optimal fertilizer recommendations, which in turn improves yield and enhances the quality of agricultural products. The current laboratory methods used to evaluate nutritional condition in fruit trees are expensive, challenging, time-consuming, and require an expert. In this study, image processing methods and neural network models was utilized to determine the stages of iron deficiency in peach trees. Therefore, a database containing 800 images of peach leaf samples was acquired. These images were then classified into four categories using the KNN clustering method: no deficiency, low deficiency, moderate deficiency, and severe deficiency. The preprocessing, feature extraction, and modeling operations were performed in the MATLAB software, version 2017. Features such as mean and standard deviation were extracted from the RGB, HSV, and Lab color space components of each image. Subsequently, the principal component analysis (PCA) algorithm was applied to the feature vector. To determine the optimal structure of the network, criteria including precision, accuracy, recall, and the F1-score were evaluated. These criteria helped ascertain the number of optimal inputs and the corresponding number of neurons for each combination of input features (PCs). Results indicated that the neural network model, structured as 6-36-4, achieved an accuracy of 89.73 ± 0.54%, precision of 89.59 ± 0.57%, recall of 89.52 ± 0.51%, and an F1-score of 89.55 ± 0.54% in detecting levels of iron deficiency in peach tree leaves. The findings from the confusion matrix and the developed model reveal that this method can effectively and efficiently detect the severity of iron deficiency in peach tree leaves. | ||
| کلیدواژه‌ها [English] | ||
| Peaches, Iron deficiency, Image processing, Neural network, KNN clustering | ||
| مراجع | ||
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