Intelligent Data Analysis with Classical Machine Learning

Punia, Sanjeev Kumar; Kumar, Manoj; Sharma, Amit

doi:10.1007/978-981-15-5566-4_71

Cited by 5 publications

(1 citation statement)

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“…Because ortho-images obtained from a UAV-equipped multispectral sensor are expensive to obtain, a sufficient number of data samples is hard to be expected. Usually one can depend on conventional computer vision approach with the small amount data rather than deep neural networks which have lots of parameters to be adjusted by training [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

2022

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Pine wilt disease (PWD) is responsible for significant damage to East Asia's pine forests, including those in Korea, Japan, and China. Preventing the spread of wilt disease requires early detection and removal of damaged trees. This paper proposes a method of detecting disease-damaged pines using ortho-images corrected from 5-band multi-spectral images captured by unmanned aviation vehicles. The proposed method relies on a ResNet18 backbone network connected to a modified DenseNet module, classifies the 5-band multispectral (RGB, NIR, Red_Edge) ortho-image patches, and visualizes the results as a heat map. The patch-based classifier was retrained with hard negative examples, after which it achieved 98.66% accuracy, an improvement over the 96.0% accuracy associated with the same method applied to RGB images. The resulting heat map reflects the approximate distribution, and movement of the disease. Disease locations are also predicted by local maximums in the heat map. When the distance between a ground truth and the predicted location is less than visible distance, e.g. about 5m, it is counted as a correct detection. The proposed detection which consists of heat map generation followed by localization achieves Recall of 93.39%, Precision of 88.26%, and F1-score of 90.75%.

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Section: Introductionmentioning

confidence: 99%