نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران.
2 گروه علوم خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران
3 استادیار گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Iron is an essential element in the growth process of plants and plays a crucial role in chlorophyll production. Iron deficiency is a serious limitation in vineyards that can significantly affect both the yield and the quality of the crop. The use of modern methods such as digital image processing not only increases precision but also reduces the need for costly and time-consuming laboratory testing, thereby lowering costs and speeding up data-driven decision-making processes in orchard management. The aim of this study is to develop a system based on image processing and neural networks to estimate the active iron content in grape leaves. For this purpose, 55 leaf samples with different levels of iron deficiency were collected and analyzed from vineyards around Urmia. The total and active iron content in the samples was measured using atomic absorption spectroscopy and the leaves were photographed and processed under controlled light conditions. Statistical features were extracted from the images and their correlation with active and total iron content was analyzed. Finally, the best features were used to predict iron content using a multilayer artificial neural network. The results of the linear regression show that active iron correlates with the R, G, H, and S color channels with coefficients of 0.64, 0.58, 0.54, and 0.45, respectively, and that total iron does not correlate with the changes in leaf color. The neural network with an optimized structure of 8-9-1 was able to predict the data from the atomic absorption device with an accuracy of 0.83, 0.88, and 0.84 for training, test, and all data, respectively. In summary, image processing can be effectively and reliably used as a tool for optimal plant nutrition management and rapid diagnosis of iron deficiency.
کلیدواژه‌ها [English]
EXTENDED ABSTRACT
Iron is an essential micronutrient that plays a crucial role in the physiological and biochemical processes of plants. Recent advances in digital imaging have encouraged various industries to use this technology to determine the relationship between color and product content. While there are several methods for determining iron concentration in plants, the use of imaging offers distinct advantages over traditional laboratory methods.
The leaf samples were taken from the young and middle leaves of the grape branches. In June 2018, 55 randomly selected samples with varying degrees of iron chlorosis were taken from several vineyards. These samples were then transported to the laboratory for imaging and analysis of active and total iron content. After the solutions were prepared for each method, the concentrations of total and active iron were determined using an atomic absorption spectrophotometer. The samples were then placed in an environment with controlled lighting for the imaging procedures. After preprocessing, the images were converted from RGB to HSV color space and statistical features were extracted from the R, G, B, H, S and V color channels. Since leaf colors vary significantly, a powerful network is needed to handle these variations. Therefore, a multilayer perceptron neural network (MLP NN) was developed to model the experimental iron data and the corresponding images. The data were split into training (70%), validation (15%) and test (15%) to prevent overfitting and reduce the dependence of the model on the training data. The color features of the leaves and the amounts of iron measured from each leaf were considered as inputs and outputs of the model, respectively. The optimization of the network structure was trained with different numbers of neurons in the layers, ranging from one to 25, using the root mean square error (R²), root mean square error (RMSE) and mean absolute percentage error (MAPE) as evaluation criteria.
The results of correlating color components with active and total iron showed that active iron correlates with the R, G, H, and S color channels with coefficients of 0.64, 0.58, 0.54, and 0.45, respectively, and that there is no correlation between changes in leaf color and total iron content. A multilayer perceptron network consisting of a hidden layer and an output layer, was used to estimate the active iron concentration using MATLAB 2018 software. The data for training, validation, and testing were selected using a random function. Tangent Sigmoid (tangsig) and linear (purelin) activation functions were applied to the hidden and output layers, respectively. The correlation between the predicted and laboratory data resulted in a coefficient of 0.84, an RMSE of 2.04, and a MAPE of 32.36. The high R2 value confirms the ability of the model to estimate the available iron in plant leaves, while the low error values underline the generalization ability of the neural network model.
The algorithm presented in this study is an effective tool for estimating plant iron. For active iron, the neural network model has shown that it is possible to estimate the amount of iron in grape leaves based on the features extracted from the image (mean and standard deviation). The neural network model developed here, based on the output of the image processing system, proved to be successful in predicting the actual active iron amounts with a detection accuracy of the algorithm of 84%. It is proposed to use databases with more samples in future studies and also to develop other machine learning and plant models for estimating active iron content.
All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.
If the study did not report any data, you might add “Not applicable” here.
The authors would like to thank all participants of the present study.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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