Deep learning approach with image noise reduction to determine planting density and defected paddy seedlings
In recent years food security issues caused by climatic changes, human resources, and production costs require a strategic approach. The emergence of artificial intelligence due to the capability of recent technology in computer processing could become a new alternative to current solutions. Opti...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/113137/1/113137.pdf |
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| Summary: | In recent years food security issues caused by climatic changes, human
resources, and production costs require a strategic approach. The emergence
of artificial intelligence due to the capability of recent technology in computer
processing could become a new alternative to current solutions. Optimal planting
density is a crucial factor for paddy cultivation as it will influence the quality and
quantity of production. There have been several studies involving planting
density using computer vision and remote sensing approach. While most of the
studies have shown a good overall performance, they have disadvantages and
room for improvement. Among the disadvantages is that the studies aim to
detect and count all the paddy seedlings to determine planting density. The
defective paddy seedling locations are not pointed out to help farmers in the
sowing process. All the previous studies for plant detection used two-stage
method object detectors like Faster R-CNN and Mask R-CNN combined with
different types of feature extractor architecture such as VGG16, ResNet,
Inception and MobileNet. Even though this type of object detector showed high
accuracy it has a high inference time. Exploring a one stage method object
detector such as Single Shot Detector could solve the high inference time for a
real-time defective crop detection and may lead towards the development of an
autonomous mobile transplanter. One stage method object detector however
tends to show a slightly lower accuracy compared to two stage method. For this
work we start with a preliminary study to evaluate the performance difference
between machine learning that involve the selection of filters as feature extractor
and deep learning technique that eliminates the requirement of hand-crafted
features engineering on the classification of paddy fields health as our based
line for the classification and detection of defected paddy seedlings. All pretrained
Deep Learning models achieved better accuracy when compared to
Machine Learning with filters for feature extraction and custom Deep Learning
Model. We then continued our study to find the best method for paddy seedlings
density classification task. All the pre-trained models showed comparable
accuracy with the Base Model when trained to classify a new object with
hyperparameter tuning and our proposed image pre-processing which reduces
noise and enhanced input images features. Transfer Learning also allowed
faster training processes by reducing the number of trainable parameters and
still capable of achieving good overall performance. The next following study is
on the detection of defected paddy seedling using both one stage and two stage
method pre-trained object detector model. The objective of the study is to
propose a method that can accurately detect and count defective paddy
seedlings to determine the sowing location. Four combinations were used, the
EfficienDet-D1 EfficientNet that utilizes Bi-directional Feature Pyramid Network
and Compound Scaling Constant method outperforming all other models. Our
image pre-processing technique managed to enhance the performance of all
state-of-the-art pre-trained object detector models. Exploratory research was
conducted to propose a robust computer vision approach to classify paddy
seedlings density and defected paddy seedlings detection using pre-trained
CNN model with transfer learning. Data augmentation, image pre-processing
and hyperparameter tuning were applied to achieve the desirable performance.
The experiment showed that pre-trained DCNN model works well for
classification and detection of a new task with a good overall performance using
transfer learning with fine tuning method. |
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