Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

Javidan, Seyed Mohamad; Banakar, Ahmad; Vakilian, Keyvan Asefpour; Ampatzidis, Yiannis; Rahnama, Kamran

doi:10.1007/s11042-024-18214-y

Cited by 4 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The devastating effects of climate change on ecosystems and human life have been widely documented, particularly in the case of crop productivity. The rising temperature has accelerated the life cycle of plant pathogens, which has resulted in an increase in their density and the emergence of mutant strains with higher pathogenicity diseases [1]. The resulting impact on agricultural products, such as crops, has led to the emergence of diseases and pests that were previously rare or specific to limited geographic areas.…”

Section: Introductionmentioning

confidence: 99%

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Talab,

Mohammadzamani,

Parashkoohi

2024

Discov Appl Sci

View full text Add to dashboard Cite

This study aimed to classify potato disease as early blight, late blight, and healthy leaves using color image processing techniques, data imbalance and feature extraction techniques. To this end, two groups of potato disease leaves with similar symptoms and one group of healthy leaves were considered. A new method based on increasing or decreasing image data using Over-Sampling and Under-Sampling techniques was used to balance them. For this purpose, after separating the diseased area from the leaf surface, the features were extracted. In total, 45 color features, 99 texture features were extracted from each of the three color spaces RGB, l*a*b and HSV, and 6 shape features were extracted from the images. Then classification was done by Random Forest. The classification accuracy results for three proposed models including classification with Original, Over-Sampling and Under-Sampling dataset were 87.89, 91.09 and 96.17%, respectively. Also, the most effective features extracted for the diagnosis of potato diseases were selected by the Relief feature selection algorithm. The results show that texture features contribute more to disease classification and data balancing techniques can increase classification accuracy. In addition, the results show that features extracted from different color spaces can improve disease diagnosis performance and help data engineers to increase the amount of features extracted and compare their performance. This study can be used in future research to classify potato diseases and other plant diseases, and its results can help researchers in their efforts.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Talab,

Mohammadzamani,

Parashkoohi

2024

Discov Appl Sci

View full text Add to dashboard Cite

show abstract

Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review

Javidan,

Banakar,

Rahnama

et al. 2024

Smart Agricultural Technology

View full text Add to dashboard Cite

Automated Assessment of Wheat Leaf Disease Spore Concentration Using a Smart Microscopy Scanning System

Doroshenko,

Golub,

Kremneva

et al. 2024

Agronomy

View full text Add to dashboard Cite

An advanced approach to the automated assessment of a microscopic slide containing spores is presented. The objective is to develop an intelligent system for the rapid and precise estimation of phytopathogenic spore concentration on microscopic slides, thereby enabling automated processing. The smart microscopy scanning system comprises an electronic microscope, a coordinate table, and software for the control of the coordinate table and image processing. The developed smart microscopy scanning system processes the entire microscope slide with multiple exposed strips, which are automatically determined based on the novel two-stage algorithm. The analysis of trained convolutional neural networks employed for the detection of spore phytopathogens demonstrates high precision and recall metrics. The system is capable of identifying and counting the number of spores of phytopathogenic fungi species Blumeria graminis, Puccinia striiformis, and Pyrenophora tritici-repentis on each exposed strip. A methodology for estimating the spore distribution on a microscopic slide is proposed, which involves calculating the average spore concentration density.

show abstract

Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

Cited by 4 publications

References 31 publications

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review

Automated Assessment of Wheat Leaf Disease Spore Concentration Using a Smart Microscopy Scanning System

Contact Info

Product

Resources

About