Rani Ruchi, Sahoo Jayakrushna, Bellamkonda Sivaiah, Kumar Sumit
Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, Kerala, 686635, India.
School of Computer Science and Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra, 411038, India.
Sci Rep. 2025 Jul 21;15(1):26461. doi: 10.1038/s41598-025-10870-w.
Precision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods. However, deep learning techniques demand a large quantity of data to train the models for diagnosis and further classification. Few-shot learning has surfaced to remove the drawbacks of deep learning methods. Therefore, the proposed work presents a novel GRCornShot model for corn disease diagnosis using few-shot learning with Prototypical Networks based on metric learning. Metric Learning calculates the distance to measure the similarity between the data points. Hence, addressing the challenge of limited labeled data, GRCornShot effectively classifies healthy and corn diseases. Furthermore, the Gabor filter is incorporated into the backbone network ResNet-50 to extract the texture features and to enhance the classification performance. The experiments show the promising application of few-shot learning in agronomic applications, providing a robust solution for detecting corn diseases precisely with minimal data requirements. Using a 4-way 2-shot, 3-shot, 4-shot, and 5-shot learning strategy, GRCornShot achieves impressive accuracy of 96.19%, 96.54%, 96.90%, and 97.89%, respectively.
精确且及时地检测植物病害对于减少作物损失和维持全球粮食安全至关重要。已经开展了大量工作来使用深度学习方法检测植物病害。然而，深度学习技术需要大量数据来训练用于诊断和进一步分类的模型。少样本学习应运而生，以消除深度学习方法的缺点。因此，所提出的工作提出了一种新颖的GRCornShot模型，用于基于度量学习的少样本学习的玉米病害诊断，使用原型网络。度量学习计算距离以衡量数据点之间的相似性。因此，GRCornShot有效解决了标记数据有限的挑战，对健康玉米和患病玉米进行了有效分类。此外，将Gabor滤波器纳入骨干网络ResNet-50以提取纹理特征并提高分类性能。实验表明少样本学习在农艺应用中的应用前景广阔，为以最少的数据需求精确检测玉米病害提供了一个强大的解决方案。使用4路2样本、3样本、4样本和5样本学习策略，GRCornShot分别实现了令人印象深刻的96.19%、96.54%、96.90%和97.89%的准确率。
