Akıllı Buzdolaplarında Meyve ve Sebzelerin Bozulmasını Algılamak için Derin Öğrenme ve IoT Tabanlı Çift Aşamalı Sistem

Öz

Sürdürülebilir bir gelecek ve yaşanabilir bir dünya sağlamak için sürdürülebilir üretim ve tüketim yöntemleri geliştirmek çok önemlidir. Ekonomik kalkınma ve sürdürülebilir yaşam, çevre ve evsel atıkları en aza indirerek ve gıda kaynaklarını verimli kullanarak sağlanabilir. Yapay zeka, bilgisayar görüşü, veri işleme ve entegre sistemler, bu tür akıllı çözümler geliştirme fırsatı sunmaktadır. Bu çalışmada, meyve ve sebzelerdeki bozulmayı erken tespit etmek için Raspberry Pi tabanlı akıllı buzdolabı modülü tasarlanmış ve uygulanmıştır. Çürümeye başlayan meyve ve sebzeler, çevrelerine çeşitli gazlar salar. Buna dayanarak, önerilen sistem iki aşamalı bir doğrulama yöntemi kullanır. İlk aşamada, buzdolaplarındaki meyve ve sebzelerin bozulması gaz sensörleri tarafından tespit edilir. Gaz sensörleri bozulmayı tespit ettiğinde, ikinci aşama tetiklenir; meyve ve sebzelerin görüntüleri ResNet50, DenseNet201, InceptionV3 ve VGG16 dahil olmak üzere CNN tabanlı modeller kullanılarak sınıflandırılır. Bozulma doğrulanırsa, belirlenen kullanıcıya bir bildirim gönderilir. Gaz algılama ile derin öğrenme tabanlı görüntü sınıflandırmasının entegrasyonu, önerilen sistemin temel yeniliğini oluşturur ve tek aşamalı yaklaşımlara kıyasla daha güvenilir ve erken algılama sağlar. Ayrıca, 20 sınıfta 12.000 görüntü içeren bir karşılaştırma veri seti üzerinde kapsamlı sınıflandırma deneyleri gerçekleştirilmiştir. Tüm CNN modellerinde ince ayar ve hiperparametre optimizasyonu gerçekleştirilmiş ve ResNet50 %98,00 ile en yüksek doğruluk oranına ulaşmıştır. Bu performans, aynı veri seti üzerinde yapılan bazı önceki çalışmalarda bildirilen sonuçları aşmaktadır. Önerilen prototip, sahip olduğu yetenekler sayesinde hem mevcut hem de yeni nesil buzdolaplarında yaygın olarak uygulanabilir.

Anahtar Kelimeler

• Çürümüş meyve ve sebze tespiti
• Derin öğrenme
• Görüntü sınıflandırma
• Raspberry Pi
• Akıllı buzdolapları

A Deep Learning and IoT-Based Dual-Stage System for Detecting Fruit and Vegetable Spoilage in Smart Refrigerators

Öz

It is critical to develop sustainable production and consumption methods to ensure a sustainable future and a livable world. Economic development and sustainable living can be achieved by minimizing environmental and household waste and by using food resources efficiently. Artificial intelligence, computer vision, data processing, and integrated systems offer the opportunity to develop such smart solutions. In this study, a Raspberry Pi-based smart refrigerator module was designed and implemented for the early detection of spoilage in fruits and vegetables. Fruits and vegetables that start to rot release various gases into the surrounding environment. Based on this, the proposed system uses a two-stage verification method. In the first stage, the spoilage of fruits and vegetables in refrigerators is detected by gas sensors. When the gas sensors detect spoilage, the second stage is triggered; images of fruits and vegetables are classified using CNN-based models, including ResNet50, DenseNet201, InceptionV3, and VGG16. If spoilage is confirmed, a notification is sent to the designated user. The integration of gas sensing with deep learning–based image classification constitutes the main novelty of the proposed system, enabling more reliable and early detection compared to single-stage approaches. Moreover, extensive classification experiments were carried out on a benchmark dataset containing 12,000 images across 20 classes. Fine-tuning and hyperparameter optimization were performed on all CNN models, with ResNet50 achieving the highest accuracy of 98.00%. This performance surpasses results reported in some of the earlier studies on the same dataset. Given its capabilities, the proposed prototype could be widely implemented in both existing and next-generation refrigerators.

Anahtar Kelimeler

• Rotten fruit and vegetable detection
• Deep learning
• Image classification
• Raspberry Pi
• Smart refrigerators

Kaynakça

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