Prediction of barberry witches’ broom rust disease using artificial intelligence models: a case study in South Khorasan, Iran

TL;DR

This study uses AI to detect a rust disease in barberry plants in Iran, achieving high accuracy to help protect crop yields.

Contribution

A deep learning model is developed for detecting barberry witches’ broom rust disease with 98% accuracy.

Findings

A CNN model was developed using TensorFlow's Keras API for disease detection.

The model achieved 98% accuracy in identifying barberry rust disease and its severity.

Cross-validation confirmed the model's robustness for distinguishing healthy and infected leaves.

Abstract

The South Khorasan Province in Iran is the main producer of seedless barberry, accounting for 98% of the country’s production. This has led to significant economic growth in the region. However, the cultivation of barberry is threatened by the rust fungus Puccinia arrhenatheri, which causes witches’ brooms on Berberis vulgaris L. var. asperma. Our research aims to detect infected leaves containing this fungal pathogen using deep learning (DL)-based artificial intelligence (AI) techniques on an available dataset. We captured healthy and infected barberry foliage images and used conventional laboratory methods to label them. We developed a convolutional neural network (CNN) deep learning model using TensorFlow’s Keras API to detect and classify barberry broom rust disease. A cross-validation technique is used to check the robustness of the proposed model. The results imply that the proposed model successfully distinguished between healthy specimens and those affected by broom rust disease. The model achieved an impressive accuracy rate of 98% in automatically identifying the disease type and its severity. This interdisciplinary research demonstrates the practical application of AI in agriculture, providing timely intervention strategies to protect crop yields and maintain economic viability in the face of plant diseases.

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