Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology

Author(s)

Bernardes, Rodrigo Cupertino ; De Medeiros, Andre ; da Silva, Laercio ; Cantoni, Leo ; Martins, Gustavo Ferreira ; Mastrangelo, Thiago ; Novikov, Arthur ; Mastrangelo, Clissia Barboza

• Document type: Journal article
• Source: AGRICULTURE-BASEL; v. 12, n. 11, p. 14-pg., 2022-11-01.

Abstract

Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by Fusarium head blight (FHB) can help to prevent human and animal health risks while improving agricultural sustainability. Robust pattern-recognition methods, such as deep learning, can achieve higher precision in detecting infected seeds using more accessible solutions, such as ordinary RGB cameras. This study used different deep-learning approaches based on RGB images, combining hyperparameter optimization, and fine-tuning strategies with different pretrained convolutional neural networks (convnets) to discriminate wheat seeds of the TBIO Toruk cultivar infected by FHB. The models achieved an accuracy of 97% using a low-complexity design architecture with hyperparameter optimization and 99% accuracy in detecting FHB in seeds. These findings suggest the potential of low-cost imaging technology and deep-learning models for the accurate classification of wheat seeds infected by FHB. However, FHB symptoms are genotype-dependent, and therefore the accuracy of the detection method may vary depending on phenotypic variations among wheat cultivars. (AU)

• FAPESP's process: 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Research Grants - Young Investigators Grants