=Paper=
{{Paper
|id=Vol-3304/paper33
|storemode=property
|title=Peach Phenology Period Identification Method Based on Ensemble Learning
|pdfUrl=https://ceur-ws.org/Vol-3304/paper33.pdf
|volume=Vol-3304
|authors=Xiaoyu Li,Aifeng Li,Peng Lan,Yaping Li,Dongyue Liang,Xiangqu Chen
}}
==Peach Phenology Period Identification Method Based on Ensemble Learning==
https://ceur-ws.org/Vol-3304/paper33.pdf
Peach Phenology Period Identification Method Based on
Ensemble Learning
Xiaoyu Li, Aifeng Li*, Peng Lan*, Yaping Li, Dongyue Liang and Xiangqu Chen
Shandong Agricultural University, College of Information Science and Engineering, Tai'an, Shandong Province,
China
Abstract
An ensemble learning-based algorithm for peach phenological stage recognition is proposed to
address the problem that peach phenological stage recognition is susceptible to low accuracy
due to interference from the complex environment of peach orchards. The model is trained on
three networks, ResNet50, AlexNet and VGG-16, on the sample images to form a classification
model. Attention mechanism was added to these three networks and the LRN layer was
changed to BN layer in addition to the AlexNet improvement. Finally, the voting method of
ensemble learning is used to improve the recognition ability of the model for images with
complex background information. The experimental results show that the recognition accuracy
of the algorithm is 99.3%, which can better achieve the accurate recognition of peach
phenology with complex backgrounds.
Keywords
Voting method, Convolutionalneural networks, Phenology identification
1. Introduction 1
The peach tree has many varieties and its fruits are delicious and loved by people for its adaptability,
short growth cycle and nutritious fruits. At present, more than 70 countries around the world plant peach
trees, and the planting range of peach trees in China is also very wide. In recent years, with the
increasing demand for peaches, the planting area is also expanding, but the lack of labor has become a
factor limiting the increase of peach production. In order to solve this problem of lack of labor to limit
the production of agricultural products, unmanned orchards and unmanned farms have come into
agricultural production. Unmanned orchards are a new production model that relies on a variety of new
technologies such as the Internet of Things, big data, and artificial intelligence to complete all farming
operations in accordance with the crop growth cycle by remotely controlling various machines [1].
Therefore, improving the recognition rate of peach phenological stages is the key to develop unmanned
peach orchards.
At present, the main research on the phenological period in China and abroad are manual observation
method, model simulation and remote sensing monitoring. Among them, Islam A S et al. constructed a
potato phenology curve by extracting NDVI and LAI in potato fields, and this study showed that remote
sensing data can show the growth of the crop [2]; Yang Q et al. used a UAV to collect high-resolution
RGB images of rice for the detection of rice phenology by a two-branch CNN architecture [3]; Zhai Deli
et al. used partial least squares to analyze the relationship between rubber tree phenology and climatic
factors, and used environmental factors as variables to predict phenology [4]. Wang X A et al. proposed
in the study of apple blossom phenology The new method of Deep Phenology and the method is
commercially viable as it can evaluate both daytime and nighttime images [5]. At present, the research
ICBASE2022@3rd International Conference on Big Data & Artificial Intelligence & Software Engineering, October 21-
23, 2022, Guangzhou, China
EMAIL: lxy18866856579@163.com(Xiaoyu Li); liaf@sdau.edu.cn(*Aifeng Li); lanpeng@sdau.edu.cn (*Peng Lan);
17862686709@163.com (Yaping Li);l411287204y@163.com(Dongyue Liang); ch_xiangqu@163.com(Xiangqu Chen)
ORCID:0000-0001-9699-4887(Xiaoyu Li);0000-0003-4427-4866(*Aifeng Li);
0000-0002-5492-524X(*Peng Lan);0000-0002-4256-1303(Yaping Li);
0000-0001-5785-6131((Dongyue Liang)); 0000-0002-3926-0390(Xiangqu Chen)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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objects of the phenological period are mainly some economic crops such as corn, wheat and rice, and
economic forest trees such as rubber trees, but the research on the phenological period of fruit trees
such as peach is less. The problem of peach phenology identification is mainly to identify the phenology
of flowers, buds and fruits of peach trees, so it is necessary to improve the model's ability to identify
peach phenology images with complex background.
For the recognition of peach phenological period, this thesis analyzes 22686 peach images, we derive
the results based on models such as AlexNet, ResNet50 and VGG16 then combine the results with
ensemble learning methods to vote on the results and finally output the prediction results.
2. Materials and Methods
In order to improve the accuracy of peach season recognition, this paper uses a combination of deep
learning and ensemble learning to build a season recognition model. The results are first trained by
multiple convolutional neural networks and then voted by the ensemble learning method to get the best
results.
The main improvement of AlexNet[6] is to use BN layer instead of LRN layer, because the local
response normalization layer is normalized locally, while BN layer is normalized for the whole batch.
The BN[7] layer is able to effectively converge the network quickly and reduce the number of iterations
significantly, while also adding the coordinate attention mechanism after the fifth layer of convolution.
The improvement for ResNet50[8] and VGG-16 [9]is the addition of the coordinate attention mechanism,
which is only added to the first and last layers of convolution. In VGG-16, the attention mechanism is
placed at the last layer of convolution.
The majority voting method is chosen in this paper for the construction of the pooling strategy. Since
the rejection of the prediction occurs in the standard absolute majority voting method, and the task of
this paper is to require the output of the prediction, the majority voting method is chosen in this paper.
For example, let the integrated model in this paper contain K base models {𝑔 , 𝑔 , ..., 𝑔 }, the type
set of all samples is {𝑐 , 𝑐 , ..., 𝑐 }, the predicted output of the base model 𝑔 on sample x is (𝑔 (x);
𝑔 (x); ... 𝑔 (x)), where 𝑔 (x )is the output of the base model 𝑔 on type 𝑐 , then the final
discrimination result of the voting method is shown in Equation (1). In majority voting, the final
predicted type is the type with the highest percentage of votes; If there is a type with the same percentage
of votes, a type is randomly selected.
𝐻 (𝑥 ) 𝑐 ∑ 𝑔 (𝑥 ) , (1)
From the above, it can be seen that this paper uses ensemble learning [10] for peach phenological
stage identification, and its process is shown in Figure 1.
Figure 1: Peach phenological stage identification process.
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3. Testing and Analysis
3.1. Data acquisition and preprocessing
The object of this paper is the various phenological stages of peaches, which are mainly
distinguished according to the status of buds, flowers and fruits, and are divided into budding stage,
flowering stage, young fruit stage, expansion stage, bagging stage and fruit ripening stage. The data in
this paper are taken from the Science and Technology Innovation Park of Shandong Agricultural
University. The data set has 22686 images, which are randomly selected and divided into training set
and test set in the ratio of 8:2.
3.2. Test environment and analysis of results
The test environment is Windows 11 operating system, CPU is 12th Gen Intel(R) Core (TM) i7-
12700H 2.70 GHz, graphics card is NVIDIA 3060, deep learning framework is Pytorch 1.12.0, and
CUDA 11.6 and CUDNN 8.4.1 are also used to accelerate the computer's computational power. The
initial learning rate was set to 0.0001; the optimizer was selected as Adam Optimizer.
The experiment in this paper is divided into 3 steps.
Step 1: The three models, AlexNet, Resnet50, and VGG-16, were trained on the prepared peach
phenology dataset. The accuracy of these three models on the dataset was 96.8%, 97.9%, and 97.8,
respectively. Then in the recognition of multi-model ensemble learning, the accuracy of integrated
learning is 98.5% and the results are shown in Figure 2.
Figure 2: Accuracy of unimproved ensemble learning models.
Step 2: The AlexNet, Resnet50 and VGG-16 models were improved by adding an attention
mechanism to each network model due to the characteristics of each weathering period of the studied
peaches, and also by using the BN layer instead of the LRN layer in the improvement of AlexNet. The
improvement process and recognition accuracy results are shown in Table 1.
Table 1: Individual model improvement process and recognition accuracy
Attention Mechanism BN Layer Accuracy of the
Network Model
（
）
（
）
Yes/No Yes/No improved model
AlexNet Yes Yes 98.8%
ResNet50 Yes No 98.7%
VGG-16 Yes No 98.8%
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From the above table, the test accuracy of AlexNet increased from 96.8% to 98.8% after adding the
attention mechanism and BN layer, Resnet50 increased from 97.9% to 98.7%, and VGG-16 increased
from 97.8% to 98.8%. This shows that adding the attention mechanism and BN layer can improve the
recognition rate of the model for sample images with complex backgrounds.
Step 3: Perform multi-model integration learning using 3 models including AlexNet, Resnet50 and
VGG-16 which have been improved.
The experiments of the algorithm in this paper were completed with the improved training and
integrated learning of the models of AlexNet, Resnet50 and VGG-16, the ensemble learning with the
unimproved models of AlexNet, Resnet50 and VGG-16 as the base models and the ensemble learning
with the improved models of AlexNet, Resnet50 and VGG-16 as the base models of the recognition
results are shown in Figure 3. The comparison of the experimental results of each model is shown in
Table 2.
From Table 2, it can be seen that the LeNet model has the worst recognition effect of 94.7% in the
individual model for peach phenological stage recognition. Then, after improving AlexNet, Resnet50
and VGG-16, the recognition accuracy increased by 2%, 0.8% and 1% respectively, which indicates
that adding attention mechanism can improve the recognition accuracy of the model for images. From
Figure 3, it can be seen that the fusion of multiple models under ensemble learning can also improve
the recognition accuracy. At the same time, after improving the recognition accuracy of individual
models, the accuracy of ensemble learning also increases from 98.5% to 99.3%, which means that
improving the recognition accuracy of individual models is also beneficial to improve the recognition
accuracy of multiple model fusion. In summary, the ensemble learning has good recognition effect for
peach phenological stage.
Table 2: The comparison of the experimental results of each model
Unchanged model into Accuracy of the improved
Network Model
accuracy model
AlexNet 96.8% 98.8%
ResNet50 97.9% 98.7%
VGG-16 97.8% 98.8%
LeNet 94.7% -
Vote-ensemble 98.5% 99.3%
Figure 3: Accuracy comparison of integrated learning model before and after improvement
4. Conclusion
In this paper, we propose a method to identify the season of peach trees with high accuracy in
complex environments, which further promotes the application of deep learning in agricultural
production. In this paper, we use the data augmentation method to increase the number of sample images,
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add the attention mechanism to AlexNet, and use BN layer instead of LRN layer to increase the
convergence speed of the model. The attention mechanism is also added to ResNet50 and VGG-16, and
the results show that the addition of the attention mechanism can effectively improve the recognition of
the waiting period. Finally, using the ensemble learning model, it can be concluded that the ensemble
model, without interfering with the results of the original model, uses the voting strategy to improve
the image recognition accuracy significantly.
The learning rate used in this paper for image recognition using the integrated model is the same
learning rate setting for all three models, and it is planned that different learning rates can be set for
recognition in the future. Since this paper is mainly to solve the problem of low accuracy of peach
season recognition, we ignore the problem of large size of the model used, so we should also consider
reducing the size of the model and making it lighter in the future. While the model is lightweight, the
accuracy of network model recognition should be kept constant or improved, so that the peach
recognition model can be more effective and practical in agricultural production and contribute to
agricultural development.
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