مروری بر ارزیابی کیفیت‌سنجی میوه و سبزیجات با استفاده از محاسبات نرم

نوع مقاله : مقاله مروری

نویسندگان

گروه مهندسی فنی کشاورزی، دانشکده فناوری کشاورزی ابوریحان، دانشگاه تهران، تهران، ایران.

چکیده

تقاضا برای محصولات باکیفیت از دهه‌های گذشته در حال افزایش بوده و هم اکنون نیز روند آن به صورت تصاعدی ادامه دارد. ارزیابی کیفیت، افزایش تولید محصولات را با استفاده از روش‌های خودکار، مقرون به صرفه و غیرمخرب تضمین می‌کند. در چند سال گذشته نتایج قابل توجهی در ارزیابی کیفیت در بخش‌های مختلف کشاورزی و غذایی به دست آمده است. این دستاوردها با استفاده از تکنیک‌های یادگیری ماشین و تحلیل تصاویر، به صورت یکپارچه‌ای یکسان شده‌اند. علاوه بر این، با وجود برنامه‌های مختلف یادگیری ماشین، این پژوهش بر روی ترکیب تکنولوژی‌های آماری ماشین‌های یادگیری و سیستم‌های بینایی ماشین در کشاورزی تمرکز کرده است. در این پژوهش، دو نوع تکنیک یادگیری ماشین، یادگیری نظارت شده و بدون نظارت، در کشاورزی استفاده شده‌اند. راه‌حل‌های نرم‌افزاری متنوعی مبتنی بر تکنیک‌های پردازش تصاویر مانند شبکه‌های عصبی مصنوعی، الگوریتم ژنتیک، یادگیری عمیق و منطق فازی برای تشخیص خودکار و طبقه‌بندی میوه‌ها درجات مختلف مورد استفاده قرار گرفته‌اند. این تکنیک‌ها به تجزیه و تحلیل داده‌ها و ارائه راه‌حل‌های بهینه برای بهبود عملکرد فرآیندها کمک می‌کنند. با استفاده از روش‌های بهینه‌سازی و شبیه‌سازی، می‌توان تا حد زیادی میزان مواد اولیه تلف شده و هزینه‌های تولید را کاهش داد. همچنین می‌توان الگوریتم‌هایی برای ارزیابی کیفیت محصولات غذایی ایجاد کرد و از اختلال در کیفیت جلوگیری کرد. در نهایت، جزئیات روش‌های مختلف درجه‌بندی محصول با استفاده از الگوریتم‌های ذکر شده و با در نظر گرفتن مزایا و معایب هر کدام در ارزیابی کیفیت محصولات باغی و کشاورزی مورد بررسی قرار می‌گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Overview of fruit and vegetables quality assessment surveys using soft computing

نویسندگان [English]

  • Mohamad Aboonajmi
  • Zohre Mostafaei
Dept of Agrotechnology, College of Aburaihan, University of Tehran, Tehran, Iran
چکیده [English]

Demand for quality products has been increasing for decades and is now increasing. Quality control ensures increased product production using an automated, cost-effective and non-destructive method. In the last few years, significant results have been achieved in various agricultural and food sectors. Despite having different programs and many different machine learning techniques, this study only explains the statistical technologies of machine learning with machine vision systems in agriculture due to the wide range of machine learning programs. Two types of machine learning techniques, such as supervised and unsupervised learning, have been used for agriculture. In this research, software solutions rely on image processing techniques such as: artificial neural networks, genetic algorithm, deep learning and fuzzy logic for automatic detection as well as classification of different degrees of fruit. According to the investigations, it was shown that fuzzy logic with 80 to 93%, 96.22% and 91.2% accuracy respectively for grading raisins based on qualitative characteristics, calculating the mass of almond kernels without breaking, grade Apple classification, also neural network has been used with 90%, 99% and 90.4% accuracy respectively to detect apple leaf disease, modelling drying kinetics of strawberry slices and detecting the severity of tomato disease using deep learning. There is also more reference to the study and description of product classification methods that using the mentioned algorithms and their relationship with the software can be a big step in quality classification of products.

کلیدواژه‌ها [English]

  • Soft computing
  • food and agriculture products
  • quality measurement
  • machine learning
  • image processing
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