GIDA BİLİMİNDE YAPAY ZEKÂ UYGULAMALARI: MAKİNE ÖĞRENMESİ TABANLI YAKLAŞIMLAR VE GÜNCEL EĞİLİMLER

Abstract

Bu çalışmada, yapay zekâ ve makine öğrenmesi tekniklerinin gıda bilimi ve teknolojisi alanındaki güncel ve potansiyel uygulamaları kapsamlı bir şekilde ele alınmıştır. Çalışmanın başlangıcında, makine öğrenmesine dair temel kavramlar, öğrenme türleri (denetimli, denetimsiz ve pekiştirmeli öğrenme) ve bu alanlarda yaygın olarak kullanılan geleneksel algoritmalar (karar ağaçları, destek vektör makineleri, yapay sinir ağları, k-en yakın komşu vb.) ayrıntılı şekilde açıklanmıştır. Devamında, doğal dil işleme (NLP), görüntü işleme ve nesnelerin interneti (IoT) tabanlı sistemler gibi yapay zekânın alt bileşenlerinin gıda bilimi alanında nasıl kullanıldığına dair örnek uygulamalara yer verilmiştir. Bu kapsamda; etiketleme, içerik analizi, sosyal medya duygu analizi, ürün tanımlama, bozulma tespiti ve gıda güvenliği izleme sistemleri gibi pek çok uygulama senaryosu incelenmiştir. Literatür incelemesine dayalı olarak, yapay zekâ tabanlı yöntemlerin kalite kontrol süreçlerinde, raf ömrü tahmininde, duyusal analizlerde, tüketici eğilimlerinin belirlenmesinde ve mikrobiyal/kimyasal bozulmaların erken tespitinde etkili biçimde kullanıldığı ortaya konmuştur. Ayrıca, bu teknolojilerin veri temelli karar destek sistemlerinin gelişimine katkı sağladığı vurgulanmıştır. Sonuç olarak, veri çeşitliliği, açıklanabilirlik ve model güvenilirliği gibi bazı sınırlılıklar bulunsa da, yapay zekânın gıda sistemlerine entegrasyonu, bu alanın geleceğinde daha akıllı, sürdürülebilir, izlenebilir ve tüketici odaklı çözümlerin önünü açacak önemli bir dönüşüm süreci olarak değerlendirilmiştir.

Keywords

• Yapay zekâ
• Makine öğrenmesi
• Derin öğrenme
• Gıda bilimi

Artificial Intelligence and Machine Learning in Food Science: Conceptual Foundations and Application Areas

Abstract

This study provides a comprehensive overview of the current and potential applications of artificial intelligence (AI) and machine learning (ML) techniques in the field of food science and technology. It begins with an explanation of key concepts related to machine learning, including learning types (supervised, unsupervised, and reinforcement learning) and commonly used traditional algorithms such as decision trees, support vector machines, artificial neural networks, and k-nearest neighbors. The study then explores how various subfields of AI—particularly natural language processing (NLP), image processing, and Internet of Things (IoT)-based monitoring systems—are applied in food science. Representative applications are presented in areas such as product labeling, content analysis, sentiment analysis on social media, product recognition, spoilage detection, and food safety monitoring systems. A literature-based review demonstrates that AI-powered methods are effectively used in critical tasks including quality control, shelf life prediction, sensory evaluation, consumer preference analysis, and early detection of microbial or chemical spoilage. Moreover, these technologies contribute to the development of data-driven decision support systems that can enhance operational efficiency across the food supply chain. Despite certain limitations such as data heterogeneity, lack of interpretability, and model reliability, the integration of AI into food systems is assessed as a transformative process. It is expected to pave the way for smarter, more sustainable, traceable, and consumer-oriented food systems in the near future.

Keywords

• Artificial intelligence
• machine learning
• deep learning
• food science

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