A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

Li, Honglei; Jin, Ying; Zhong, Jiliang; Zhao, Ruxing

doi:10.1155/2021/6868592

Cited by 19 publications

(4 citation statements)

References 31 publications

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“…In order to improve the generalization performance of the recognition method, researchers have introduced ensemble learning to image-based crop disease recognition ( Li et al., 2021 ). Ensemble learning ( Ganaie et al., 2022 ) is an effective way to improve the generalization performance of the recognition method.…”

Section: Related Workmentioning

confidence: 99%

A novel ensemble learning method for crop leaf disease recognition

He,

Zhang,

Gao

2024

Front. Plant Sci.

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Deep learning models have been widely applied in the field of crop disease recognition. There are various types of crops and diseases, each potentially possessing distinct and effective features. This brings a great challenge to the generalization performance of recognition models and makes it very difficult to build a unified model capable of achieving optimal recognition performance on all kinds of crops and diseases. In order to solve this problem, we have proposed a novel ensemble learning method for crop leaf disease recognition (named ELCDR). Unlike the traditional voting strategy of ensemble learning, ELCDR assigns different weights to the models based on their feature extraction performance during ensemble learning. In ELCDR, the models’ feature extraction performance is measured by the distribution of the feature vectors of the training set. If a model could distinguish more feature differences between different categories, then it receives a higher weight during ensemble learning. We conducted experiments on the disease images of four kinds of crops. The experimental results show that in comparison to the optimal single model recognition method, ELCDR improves by as much as 1.5 (apple), 0.88 (corn), 2.25 (grape), and 1.5 (rice) percentage points in accuracy. Compared with the voting strategy of ensemble learning, ELCDR improves by as much as 1.75 (apple), 1.25 (corn), 0.75 (grape), and 7 (rice) percentage points in accuracy in each case. Additionally, ELCDR also has improvements on precision, recall, and F1 measure metrics. These experiments provide evidence of the effectiveness of ELCDR in the realm of crop leaf disease recognition.

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Section: Related Workmentioning

confidence: 99%

A novel ensemble learning method for crop leaf disease recognition

He,

Zhang,

Gao

2024

Front. Plant Sci.

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“…Types of tree damage found included open wounds, resinosis or gummosis, broken stems, loss of dominant shoots, broken or dead branches, damaged buds, and discolored odors. This condition can be caused by a tall and dense canopy that makes the humidity high, which results in the crown being affected by disease [28]. The type of damage and amount are presented in Table 4 as follows.…”

Section: Tree Vitalitymentioning

confidence: 99%

The Influence of Community Participation on Health Trends in the Mangrove Forest of Labuhan Maringgai District, East Lampung Regency

Nurfaizi,

Safe’i

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The health trend of mangrove forests is influenced by community participation around the forest. Communities have a role in managing mangrove forests. The purpose of this study was to determine the effect of community participation on the health trend of mangrove forests. The stages of this research are: Assessment of forest health based on ecological indicators and data analysis using the FHM method; collection of participation data with questionnaires to the public; and data analysis using the multiple linear regression method. Based on the results of the study, the final value of forest health at CL 1 was 4.11 (bad), CL 2 was 6.95 (good), CL 3 was 6.69 (good), and CL 4 was 7.4 (good). The average value for forest health is 6.29 in the moderate category. The significant values obtained are.023* (preparation),.321 (implementation), and.851 (evaluation). The planning aspect has an influence on the health condition of mangrove forests. Community participation affects the condition of forest health from an ecological perspective. So the role of the community is very important to support the preservation of mangrove forests.

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“…The prevention of fruit tree diseases can also significantly impact overall agricultural production. Li et al [102] conducted a study comparing the performance of three ensemble learning classifiers and two deep learning classifiers. The stacking ensemble learning classifier provides the highest validation accuracy of 98.05% and test accuracy of 97.34% on a dataset of 10,000 images of pear black spot, pear rust, apple mosaic, and apple rust.…”

Section: Big Data and Artificial Intelligencementioning

confidence: 99%

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Yadav

Kaushik

Sharma³

et al. 2022

AgriEngineering

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Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies.

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A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

Cited by 19 publications

References 31 publications

A novel ensemble learning method for crop leaf disease recognition

A novel ensemble learning method for crop leaf disease recognition

The Influence of Community Participation on Health Trends in the Mangrove Forest of Labuhan Maringgai District, East Lampung Regency

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

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