CLASSIFICATION OF INSECT PESTS WITH DEEP LEARNING AND IMAGE PREPROCESSING TECHNIQUES

Abstract

Early and effective insect pest detection and control help to protect plants, increase crop yields, and reduce losses in the agricultural economy. In this paper, deep learning-based methods are proposed for classifying insect pests. Additionally, the impact of various image preprocessing techniques on performance has been investigated. The proposed models utilize transfer learning with five different pre-trained deep neural networks (GoogLeNet, ResNet-18, ResNet-101, VGG-16, and VGG-19), and Support Vector Machine classifier with features extracted from these networks. Furthermore, a comprehensive performance analysis is conducted using various image preprocessing techniques such as green color channel extraction, data augmentation, histogram equalization, and background elimination with deep learning based segmentation, individually and in combination. The experiments were conducted on the Li and D0 datasets containing 10 and 40 insect pest species, respectively. As a result of the experiments, the highest performances were obtained on both datasets, with accuracy rates of 96.36% and 99.63%, respectively, using data augmentation and transfer learning with the ResNet-101 network. The experimental results indicate that the proposed models can be effectively used in insect pest control.

Keywords

• Deep Learning
• Transfer Learning
• Image Processing
• Insect Pest Classification
• Smart Agriculture.

GÖRÜNTÜ ÖN İŞLEME TEKNİKLERİ VE DERİN ÖĞRENME İLE BİTKİ ZARARLILARININ SINIFLANDIRILMASI

Abstract

Bitki zararlılarının erken dönemde, etkili bir şekilde tespit edilip kontrol altına alınmalarını sağlamak bitkilerin korunmasına, ürün veriminin artırılmasına ve tarım ekonomisindeki kayıpların azaltılmasına yardımcı olmaktadır. Bu çalışmada, bitki zararlılarının sınıflandırılması için derin öğrenme tabanlı yöntemler önerilmiştir. Aynı zamanda çeşitli görüntü ön işleme tekniklerinin performansa etkisi araştırılmıştır. Önerilen modeller, önceden eğitilmiş beş farklı derin sinir ağı (GoogLeNet, ResNet-18, ResNet-101, VGG-16 ve VGG-19) ile transfer öğrenimi ve bu ağlardan çıkarılan öznitelikler ile Destek Vektör Makinesi sınıflandırıcısını kullanmaktadır. Ayrıca yeşil renk kanalı çıkarımı, veri artırımı, histogram eşitleme, derin öğrenme tabanlı segmentasyon ile arka plan eliminasyonu gibi farklı görüntü ön işleme teknikleri ayrı ayrı ve birlikte kullanılarak kapsamlı bir performans analizi yapılmıştır. Deneyler, sırasıyla 10 ve 40 bitki zararlısı türü içeren Li ve D0 veri setleri üzerinde gerçekleştirilmiştir. Deneyler sonucunda iki veri setinde de veri artırımı ve ResNet-101 ağı ile transfer öğrenimi yöntemi kullanılarak sırasıyla %96.36 ve %99.63 doğruluk ile en yüksek performanslar elde edilmiştir. Deneysel sonuçlar, önerilen modellerin bitki zararlısı kontrolünde etkin bir şekilde kullanılabileceğini göstermektedir.

Keywords

• Derin Öğrenme
• Transfer Öğrenimi
• Görüntü Ön İşleme
• Bitki Zararlısı Sınıflandırma
• Akıllı Tarım

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