LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance. However, they are very difficult to obtain and are not always available in practice. Deep learning-based techniques, and particularly generative adversarial networks (GANs), can be applied to generate high-quality super-resolution images, but these methods often produce unexpected artifacts that can lower the diagnostic performance. In this paper, we propose a novel artifact-suppression super-resolution method that is specifically designed for diagnosing leaf disease, called Leaf Artifact-Suppression Super Resolution (LASSR). Thanks to its own artifact removal module that detects and suppresses artifacts to a considerable extent, LASSR can generate much more pleasing, high-quality images compared to the state-of-the-art ESRGAN model. Experiments based on a five-class cucumber disease (including healthy) discrimination model show that training with data generated by LASSR significantly boosts the performance on an unseen test dataset by nearly 22% compared with the baseline, and that our approach is more than 2% better than a model trained with images generated by ESRGAN.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticSuper-ResolutionSimilar Papers 제목 키워드 기반
Super-Resolution for Practical Automated Plant Disease Diagnosis System
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost. In this paper, we first…
DiagnosticSuper-ResolutionA comprehensive review on Plant Leaf Disease detection using Deep learning
Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems hav…
Deep LearningPrognosisSuper-ResolutionDetection of healthy and diseased crops in drone captured images using Deep Learning
Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detec…
image-classificationImage ClassificationStateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection
Self-supervised learning (SSL) is attractive for plant disease detection as it can exploit large collections of unlabeled leaf images, yet most existing SSL methods are built on CNNs or vision transformers that are poorl…
Self-Supervised LearningRepresentation LearningMulti-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. Thi…
Management