Bitki hastalığı tespiti için CAM tabanlı açıklanabilirlik yöntemlerinin yerelleştirme değerlendirmesi

Öz

Son yıllarda bilgisayarla görme teknolojileri, hassas tarımda kritik bir rol oynamış, robotik ve yapay zekayı kullanarak mahsul üretiminde görevleri otomatikleştirmiştir. Görüntü tabanlı uygulamalar umut vadetse de, modelin yorumlanabilirliği önemli bir zorluk olmaya devam etmektedir. Açıklanabilir yapay zeka, bitki bilimcilerine yorumlanabilir ve güvenilir bilgiler sunarak bitki hastalıklarının anlaşılmasını geliştirmeyi hedeflemektedir. Bu çalışma, açıklanabilirlik metriklerinin model değerlendirmesine entegrasyonuna odaklanmakta ve bitki hastalığı sınıflandırma modellerine uygulanan açıklanabilirlik yöntemlerinin detaylı bir analizini sunmaktadır. EfficientNet, MobileNet, ResNet ve ShuffleNet gibi mimarilerle, açık bir bitki hastalığı veri seti üzerinde eğitilmiş Sınıf Aktivasyon Haritalama tabanlı görselleştirme yöntemleri kullanılarak hem sınıflandırma başarısı hem de modelin açıklanabilirliği değerlendirilmiştir. Lokalizasyon sonuçları, dikkat haritalarının hastalıklı bölgeleri etiketleyen sınırlayıcı kutularla ne kadar uyumlu olduğunu enerji tabanlı bir perspektiften değerlendirerek elde edilmiştir. Bulgular, görüntülerden çıkarılan özelliklerin boyutları ve konumlarının sınıflandırma sonuçlarını önemli ölçüde etkilediğini göstermektedir ve veri etiketleme aşamasında doğru anotasyonların önemini vurgulamaktadır. Bu çalışma, hastalık tespitindeki olası yanlılıkları ortaya çıkarmakta ve derin öğrenme modellerinin değerlendirilmesinde açıklanabilirlik metriklerinin gerekliliğini vurgulayarak, bitki hastalıklarının daha doğru ve verimli bir şekilde tespit edilmesi için derin öğrenme tekniklerinin optimize edilmesine zemin hazırlamaktadır.

Anahtar Kelimeler

• hassas tarım
• bilgisayarlı görme
• derin öğrenme
• bitki hastalığı sınıflandırması
• açıklanabilirlik

Localization evaluation of CAM based explainability techniques for plant disease detection

Abstract

In recent years, computer vision technologies have played a critical role in precision agriculture, leveraging robotics and artificial intelligence to automate tasks in crop production. While image-based applications hold promise, model interpretability remains a significant challenge. Explainable artificial intelligence aims to address this by providing plant scientists with interpretable, reliable information, improving the understanding of plant diseases. This study focuses on integrating explainability metrics into model evaluation, with a detailed analysis of explainability methods applied to plant disease classification models. Using Class Activation Mapping based visualization methods with architectures such as EfficientNet, MobileNet, ResNet, and ShuffleNet, trained on a public plant disease dataset, the study assessed both classification success and model explainability. Localization results were derived from an energy-based perspective, assessing how well saliency maps aligned with bounding boxes of diseased areas. The findings reveal that feature dimensions and positions in the images significantly influence classification outcomes, highlighting the importance of precise annotations during data labeling. This study uncovers potential biases in disease detection and emphasizes the need for explainability metrics in evaluating deep learning models, paving the way for more accurate and efficient plant disease detection techniques.

Keywords

• precision agriculture
• computer vision
• deep learning
• plant disease classification
• explainability

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