POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea

Abstract

Chickpeas (Cicer arietinum L.) are a nutritious legume crop farmed on 17.8 million hectares in 56 countries throughout the world, with an estimated yearly yield of 14.78 million tones. Ethiopia is the leader in chickpea production on the African continent and the sixth-largest producer globally. However, Ascochyta rabiei remains a serious disease of chickpeas. If Ascochyta rabies is not managed, its effects on chickpea output could be either partial or complete under favourable environmental conditions. Knowing the severity level of this disease in farmlands where chickpeas are grown has an impact on the rates of yield and quality losses. Currently, Ethiopian farmers and pathologists in the field use traditional procedures to figure out the severity of Ascochyta blight, lead to invalid fungicide treatment. In this work, we created customized version of VGGNet model to identify the Ascochyta blight’s severity level. For noise reduction, we combined the Gaussian and Adaptive Median Filters; for optimization, we employed the Pelican Optimization Algorithm (POA). The model categorizes the chickpea images into five groups according to the severity of the disease: Asymptomatic, Resistant, Moderately Resistant, Susceptible, and Highly Susceptible. The study’s findings indicate that the customized VGGNet outperformed the other models, achieving an accuracy of 96%.

Keywords:

• Chickpea
• ascochyta blight of chickpea
• computer vision
• image classification VGGNet models

REVIEWING EDITOR:

• Manuel Tejada, Universidad de Sevilla, Spain

SUBJECTS:

• Agriculture and Food
• Artificial Intelligence
• Information & Communication Technology (ICT)

Authors’ contributions

Melaku Bitew Haile and Abebech Jenber Belay were responsible for the Study conception and design, data collection, analysis and interpretation of results, and draft manuscript preparation. Melaku Bitew Haile was the supervisor of the research. All authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The author wants to declare that they can submit the data at any time based on publisher’s request. The datasets used and/or analyzed during the current study will be available from the author on reasonable request.

Additional information

Funding

The authors received no funding for this research.

Notes on contributors

Melaku Bitew Haile

Mr. Melaku Bitew Haile received the B.Sc. degree in July 2017 and the M.Sc. degree in July 2021 in Information Technology from the University of Gondar, Gondar, Ethiopia. His research interests include cyber security, deep learning, image processing, machine learning, and artificial intelligence. Mr. Melaku serves as a reviewer for an international journal. His research has been published in major international journals. Presently, Mr. Melaku works at the University of Gondar in the department of Information Technology.

Abebech Jenber Belay

Abebech Jenber Belay received the B.Sc. degree in JUNE, 2016 at Debre Markos University, Ethiopia in Information Technology and M.Sc. degree in JANUARY, 2021 in Information Technology from the University of Gondar, Ethiopia. Her research interests include Data Science, Deep learning, Image processing, Machine learning, Cyber security, and Artificial Intelligence. Currently, she is lecturer at the University of Gondar in the department of Information Technology.