طبقه بندی ارقام سیب زمینی بر اساس چقرمگی بوسیله شبکه عصبی مصنوعی و روش LDA

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه محقق اردبیلی، اردبیل، ایران

2 گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

3 مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

4 گروه مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران

چکیده

در پاسخگویی به یکی از بزرگ ترین چالش های قرن حاضر یعنی برآورد نیاز غذایی جمعیت در حال رشد، تکنولوژی های پیشرفته‌ای در کشاورزی کاربرد پیدا کرده است. سیب زمینی، یکی از مواد غذایی اصلی در رژیم غذایی مردم جهان است. لذا مطالعه روی جنبه‌های مختلف آن، از اهمیت زیادی برخوردار است. به دلیل تعدد زیاد واریته های این محصول و برخی مواقع عدم آشنایی احدهای فرآوری با ارقام آن و نیز وقت گیر بودن و عدم دقت زیاد در شناسایی ارقام مختلف سیب زمینی توسط کارشناسان و زارعین، و اهمیت شناسایی ارقام سیب زمینی و نیز سایر محصولات کشاورزی در هر مرحله از پروسه‌ی صنایع غذایی، نیاز به روش هایی برای انجام این کار با دقت و سرعت کافی، ضروری می باشد. این مطالعه با هدف استفاده از خواص مکانیکی همراه با روش های کمومتریکس از جمله LDA و ANN به عنوان یک روش سریع و ارزان برای تشخیص ارقام مختلف سیب زمینی انجام شد. در پژوهش حاضر ، از دستگاه سنتام موجود در دانشگاه محقق اردبیلی جهت تعیین خواص مکانیکی استفاده شد. بر اساس نتایج به دست آمده برای تشخیص رقم با روش های مذکور دقت روشهای LDA و ANN بالای 70 % به دست آمد.

کلیدواژه‌ها


عنوان مقاله [English]

Classification of potato cultivars based on Toughness coupled with ANN and LDA methods

نویسندگان [English]

  • Ali Khorramifar 1
  • Mansour Rasekh 2
  • Hamed Karami 3
  • Aref Mardani Korani 4
1 University of Mohaghegh Ardabili , Ardabil , Iran
2 University of Mohaghegh Ardabili, Ardabi, Iran
3 University of Mohaghegh Ardabili, Ardabil, Iran
4 Urmia university, Urmia, Iran
چکیده [English]

Introduction
Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and after wheat, rice and corn, it is the fourth product in the food basket of human societies. According to the statistics of the Food and Agriculture Organization of the United Nations, the area under cultivation of this crop in Iran in 2017 was 161 thousand hectares and the crop harvested from this area is about 5.1 million tons. Traditional methods of determining potato varieties were based more on morphological features, but with the production of new products, there was a need for methods that were faster and more recognizable. Meanwhile, high-performance artificial neural network can be used to classify cultivars. Artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified using LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.
By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.

Methodology
First, 5 different potato cultivars were prepared from Ardabil Agricultural Research Center and kept at a temperature of 4-10 ° C. One day later, 21 samples of each potato cultivar were prepared using a cutting cylinder and then data were collected. To determine the toughness of the samples, the Centam device available in Mohaghegh Ardabili University was used. Each potato cultivar was subjected to compressive force at three levels of loading speed of 10, 40 and 70 mm / min with 7 repetitions. Then the amount of toughness was calculated according to Equation (1). Then linear diagnostic analysis (LDA) and artificial neural networks (ANN) were used to classify potato cultivars. LDA is a supervised method used to find the most distinctive special vectors, maximizing the ratio of variance between class and within the class, and being able to classify two or more groups of samples. ANN and pattern recognition were used to find similarities and differences in the classification of potato cultivars. For this, 1 neuron was considered for the input layer, the hidden layer with the optimal number of neurons will be considered and five output neurons with Depending on the number of output classes the target will be considered. In network training, the logarithmic sigmoid transfer function and Lunberg-Marquardt learning method were used (Figure 4), and the error value was calculated using the mean square error. For learning (70%), testing (15%) and validation (15%) all data were randomly selected. Training data was provided to the network during the training and the network was adjusted according to their error. Validation was used to measure network generalization and completion of training. Data testing had no effect on training and therefore provided an independent measurement of network performance during and after training. All of the calculations and matrix classification were performed using MATLAB R2018a and X 10.4 Unscrambler software.
Toughness in 5 different potato cultivars was obtained using Centam machine and Equation 1. The values obtained for the toughness of 5 potato cultivars were analyzed using Mstatc software. The results of analysis of variance were significant for the toughness of 5 different potato cultivars at the level of 1% and its coefficient of variation was 2.28. LDA and ANN methods were used to detect potato cultivars based on the values calculated for toughness. Detection results of cultivars using LDA were equal to 70.48% (Figure 6). Also, the accuracy of ANN method according to the perturbation matrix was equal to 72.4% (Figure 7).
Conclusion
In this study, the amount of toughness for 5 different potato cultivars was calculated using Centam machine available in Mohaghegh Ardabili University with the help of Equation 1. Chemometrics methods including LDA and ANN were used for qualitative and quantitative analysis of data to identify and classify potato cultivars. Thus, LDA and ANN were able to identify and accurately classify different potato cultivars with an accuracy of over 70%. The obtained toughness has the ability to be used as a method to distinguish different potato cultivars. The use of this method in identifying potato cultivars will be very useful for factories such as chips factory and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and stiffness with the help of chemometrics methods to optimize production and The processing of agricultural products should be used in the food industry, which has led to more customer friendliness and, in addition, can reduce agricultural waste.

کلیدواژه‌ها [English]

  • Potato
  • Toughness
  • Artificial Neural Network
  • Classification
  • LDA