A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

Honglei Li,Ying Jin,Jiliang Zhong,Ruixue Zhao,Sampath Pradeep

doi:10.1155/2021/6868592

Abstract

Fruit tree diseases have a great influence on agricultural production. Artificial intelligence technologies have been used to help fruit growers identify fruit tree diseases in a timely and accurate way. In this study, a dataset of 10,000 images of pear black spot, pear rust, apple mosaic, and apple rust was used to develop the diagnosis model. To achieve better performance, we developed three kinds of ensemble learning classifiers and two kinds of deep learning classifiers, validated and tested these five models, and found that the stacking ensemble learning classifier outperformed the other classifiers with the accuracy of 98.05% on the validation dataset and 97.34% on the test dataset, which hinted that, with the small- and middle-sized dataset, stacking ensemble learning classifiers may be used as cost-effective alternatives to deep learning models under performance and cost constraints.

Highlights

In recent years, due to the influence of global climate and environmental changes, crop disasters around the world occur more frequently than ever, which results in a significant decline of the yield and quality of agricultural products, especially of fruit products
In 1990s, intelligent expert systems were developed to treat with agricultural problems. e various intelligent technologies were introduced to expert systems to improve the accuracy, intelligence, and practicability of disease diagnosis
Compared with Fully Convolutional Networks (FCNs), SegNet, U-NET, and DenseNet, the accuracy of the proposed model was increased by 13.00%, 10.74%, 10.40%, 10.08%, and 6.40%, respectively, and the training time was reduced by 0.9 hours [15]

Summary

Introduction

Due to the influence of global climate and environmental changes, crop disasters around the world occur more frequently than ever, which results in a significant decline of the yield and quality of agricultural products, especially of fruit products. Professionals used electronic microscopes and other equipment to observe bacterial changes, such as enzyme-linked immunosorbent Assays, DNA probe technology, PCR technology, and other biological methods [3,4,5]. Those recognition methods cannot be widely practiced due to the large investment of instruments and equipment, and high cost of time and labors. PLANT/DS, as a kind of expert system, was developed to diagnose soybean diseases and insect pests [6]. In recent 10 years, machine learning, especially deep learning, helps with plant disease diagnosis based on images recognition. In recent 10 years, machine learning, especially deep learning, helps with plant disease diagnosis based on images recognition. is paper aims at proposing a machine learning-based model for fruit disease diagnosis

Related Studies

The Data Source and Feature Engineering

Model Training and Selection

Results and Discussion

Conclusions and Future Studies