تشخیص و شناسایی ارقام سیب زمینی با ماشین بویایی بوسیله روش ‌های شبکه عصبی مصنوعی و lda

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 بوده و گیاهی است مهم که در سراسر جهان رشد می کند و به عنوان یک محصول مهم در کشورهای در حال توسعه و توسعه یافته برای رژیم غذایی انسان به عنوان یک منبع کربوهیدرات، پروتئین، و ویتامینها به حساب می آید. به دلیل تعدد زیاد واریته های این محصول و برخی مواقع عدم آشنایی واحدهای 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 ماشین بویایی همراه با روش های lda و شبکه عصبی مصنوعی به عنوان روش سریع و ارزان برای تشخیص ارقام مختلف سیب زمینی انجام شد. بر اساس نتایج به دست آمده برای تشخیص رقم با روش های مذکور دقت روشهای lda و ann 100 % به دست آمد.

detection and identification of potato cultivars by olfactory machine by artificial neural network and lda methods

potato is an important vegetable that grows all over the world and is considered an important product in developing and developed countries for the human diet as a source of carbohydrates, proteins, and vitamins. this product is native to south america and its origin is from peru, 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. in the meantime, the high-performance artificial neural network can be used to classify cultivars. artificial neural networks can classify and detect cultivars, are flexible, and are used in most agricultural products. therefore, the olfactory machine can have high efficiency in classifying and distinguishing cultivar, originality and storage time. the olfactory machine is a system that has a different structure and approach from other methods (image processing, neural network, etc.), is flexible and is used in most agricultural products due to the presence of odour in them. with the rapid and rapid advancement of computer technology and sensor technology, the application of the bionic electronic nose, including a semiconductor gas-sensitive sensor and a pattern recognition system as a means of detection, offers a new method for rapid classification and digit recognition. give. the electronic nose has also introduced a new method for classifying and detecting rough rice in a non-destructive and fast way. due to a large number of potato varieties and sometimes the lack of familiarity of processing units with its cultivars and also time-consuming and inaccurate in identifying different potato cultivars by experts and farmers, and the importance of identifying potato cultivars and other agricultural products in at every stage of the food industry process, it is necessary to find ways to do this accurately and quickly enough. the aim of this study was to evaluate the ability and accuracy of the electronic nose with the help of an artificial neural network to detect and differentiate several potato cultivars.