Deep-Learning Approach for <i>Fusarium</i> Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology
Rodrigo Cupertino Bernardes | André De Medeiros | Laercio da Silva | Leo Cantoni | Gustavo Ferreira Martins | Thiago Mastrangelo | Arthur Novikov | Clíssia Barboza Mastrangelo
Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by <i>Fusarium</i> 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.
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