Abstract
Accurate and timely identification of plant leaf diseases is essential for resilient and sustainable agriculture, yet most deep learning approaches rely on large annotated datasets and computationally intensive models that are unsuitable for data-scarce and resource-constrained environments. To address these challenges we present a few-shot learning approach within a lightweight yet efficient framework that combines domain-adapted MobileNetV2 and MobileNetV3 models as feature extractors, along with a feature fusion technique to generate robust feature representation. For the classification task, the fused features are passed through a Bi-LSTM classifier enhanced with attention mechanisms to capture sequential dependencies and focus on the most relevant features, thereby achieving optimal classification performance even in complex, real-world environments with noisy or cluttered backgrounds. The proposed framework was evaluated across multiple experimental setups, including both laboratory-controlled and field-captured datasets. On tomato leaf diseases from the PlantVillage dataset, it consistently improved performance across 1 to 15 shot scenarios, reaching 98.23+-0.33% at 15 shot, closely approaching the 99.98% SOTA benchmark achieved by a Transductive LSTM with attention, while remaining lightweight and mobile-friendly. Under real-world conditions using field images from the Dhan Shomadhan dataset, it maintained robust performance, reaching 69.28+-1.49% at 15-shot and demonstrating strong resilience to complex backgrounds. Notably, it also outperformed the previous SOTA accuracy of 96.0% on six diseases from PlantVillage, achieving 99.72% with only 15-shot learning. With a compact model size of approximately 40 MB and inference complexity of approximately 1.12 GFLOPs, this work establishes a scalable, mobile-ready foundation for precise plant disease diagnostics in data-scarce regions.
Abstract (translated)
准确且及时地识别植物叶片疾病对于建立有弹性和可持续的农业至关重要，然而大多数深度学习方法依赖于大规模标注数据集和计算资源密集型模型，在数据匮乏和资源受限环境中并不适用。为了解决这些问题，我们提出了一种轻量级但高效的框架内的少量样本学习（few-shot learning）方法，该框架结合了领域适应的MobileNetV2和MobileNetV3模型作为特征提取器，并采用特征融合技术生成稳健的特征表示。对于分类任务，融合后的特征通过增强注意力机制的Bi-LSTM分类器传递，以捕获序列依赖性并聚焦于最相关的特征，从而在复杂且背景嘈杂的真实世界环境中实现最优分类性能。 该框架在多个实验设置中进行了评估，包括实验室控制和野外捕捉的数据集。在PlantVillage数据集中番茄叶片疾病的测试中，在1到15个样本的场景下其表现均得到提升，并在15个样本时达到98.23±0.33%的准确率，接近使用带有注意力机制的归纳LSTM实现的99.98%的最佳性能（SOTA），同时保持轻量级和移动友好性。在真实世界条件下使用Dhan Shomadhan数据集中的田野图像时，在15个样本的情况下，其表现仍然稳健，达到了69.28±1.49%，并在复杂背景中表现出强大的适应能力。值得注意的是，它还超越了PlantVillage数据集中六种疾病的SOTA准确率（96.0%），在仅使用15个样本学习时就实现了99.72%的准确度。 此研究工作以约40MB的小型模型和大约1.12GFLOPs的推理复杂性建立了一个可扩展且适用于移动设备的基础框架，为数据匮乏地区的精准植物病害诊断奠定了基础。
URL
https://arxiv.org/abs/2512.13428
