A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

By Juncheng Ma, K. Du, F. Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun. Computers and Electronics in Agriculture, 2018

464 citations recorded by the source corpus.

Abstract Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/e91e79f95aa560a5c6ace17ccdd87676aa5820aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

Citations in the corpus

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “It yi ld bett r results than classifiers, namely, Support-Vector Machi es (SVM), Sensors 2021, 21, 4749 14 of 34 Random Forest, and AlexNet [37].”
Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, background): “This process is time-consuming, challenging, and most of the time results in incorrect diagnosis with unsuitable exercise of the pesticides [4].”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “Detection of four cucumber diseases was done in [66] and accuracy was compared with Random Forest, Support Vector Machines, and AlexNet models.”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, unknown): “Ma et al. [11] used a deep CNN to conduct symptom-wise recognition of four cucumber diseases (i.e., downy mildew, anthracnose, powdery mildew, and target leaf spots).”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, unknown): “S89 [127] 2018 3 AlexNet 93,4% Customized 1.”
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, unknown): “Liu et al. (2017) created a dataset of apple leaves and proposed a new architecture based on AlexNet for the recognition of diseases and Ma et al. (2018) developed a dataset with images of common disease symptoms aﬀecting cucumber leaves.”
Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, background): “In the studies on plant disease classiﬁcation, accuracy is the most common evaluation index [53,60,64,67,68].”