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Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images
Pengliang Wei1, Ting Jiang1, Huaiyue Peng1
1Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China.
Plant Phenomics (Washington, D.C.)
|December 14, 2020
Summary
This study introduces a new method combining OTSU binarization and convolutional neural networks (CNNs) for accurate coffee flower identification using time-lapse images. The approach significantly improves small plant monitoring in agriculture.
Area of Science:
- Agricultural remote sensing
- Computer vision
- Machine learning
Background:
- Crop-type identification is crucial for agricultural remote sensing, impacting yield prediction and field management.
- Current satellite and UAV platforms struggle with accurate monitoring of small targets like coffee flowers.
- Ground-based time-lapse imaging offers high spatial-temporal resolution for small-scale plantation monitoring.
Purpose of the Study:
- To enhance coffee flower identification accuracy using time-lapse digital images.
- To develop a robust method for small target monitoring in agricultural settings.
- To evaluate the proposed method against existing machine learning models.
Main Methods:
- A hybrid approach combining the OTSU binarization algorithm and a convolutional neural network (CNN) model.
- Utilizing VGGNet for pre-training and initializing the CNN model.
- Training the CNN with selected positive and negative coffee flower samples from digital images.
- Optimizing boundary information using binarization results after initial CNN extraction.
Main Results:
- The proposed method demonstrates improved coffee flower classification accuracy compared to Support Vector Machine (SVM) and standalone CNN models.
- Optimal performance was achieved under specific conditions: a 52.5° depression angle and soft lighting.
- The method reached a Dice (F1) score of 0.80 and an Intersection over Union (IoU) of 0.67 under optimal conditions.
Conclusions:
- The combined OTSU binarization and CNN approach effectively improves coffee flower identification accuracy from time-lapse images.
- This method provides a viable solution for monitoring small agricultural targets, overcoming limitations of traditional remote sensing platforms.
- Further research can explore variations in imaging conditions and model architectures for broader applicability.
