A Hybrid Multistage Model Based on YOLO and Modified Inception Network for Rice Leaf Disease Analysis
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Lack or excess of water, moisture, and nutrients may cause diseases in various growing stages of rice. Unlike related studies, this work aims to detect each disease's symptom separately, rather than just classifying images by a classifier or showing the whole diseased leaf in a single bounding box. In this way, we consider all disease regions and make it possible to observe better the disease progression by considering detected boxes. Our motivation for this hybrid study stems from the fact that more than one disease symptom may occur on a leaf and the detection of symptoms at an early stage can positively affect the harvest yield. The main aim of this study is to classify rice leaf disease images accurately, reduce false detections, and validate the predictions of the classification network utilizing an object detection network. Therefore, we identify two stages for this work. In the first part, the task of classification, and in the second part, the task of determining the location of the disease symptoms is conducted. We use data augmentation and disout techniques to prevent overfitting in the classification process and to improve performance by modifying the classification network. Finally, we discuss how classification robustness can be tested and false predictions can be eliminated using the classification network Inception v3 and the detection network YOLOv5x jointly. As a result of the proposed hybrid model, state-of-the-art results are achieved with 96.67 % accuracy and 98.24 % F1 score on the publicly available rice leaf disease dataset.
Açıklama
Anahtar Kelimeler
Deep Learning, Rice, Disout, Regularization, Smart Agriculture, Classification
Kaynak
Arabian Journal For Science And Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
49
Sayı
5
