Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition

Abstract

Wheat serves as a crucial agricultural commodity and a primary dietary staple for numerous global populations. However, it faces persistent threats from various diseases targeting wheat leaves, ultimately impacting its production. Accurate and prompt automated disease diagnosis through advanced computer vision is crucial for safeguarding wheat quality. However, the literature relied on inadequate feature selection acquired from computationally expensive backbones followed by shallow layered networks. It is resultantly limiting their capacity to recognize and prioritize diseased areas effectively. Therefore, this paper introduces an optimal features-assisted lightweight framework that integrates EfficientNet-B3 with a spatial attention (SA) mechanism to capture healthy and unhealthy patterns effectively. The proposed framework harnesses this capability to address the vital regions affected by wheat diseases via an optimized, lightweight, and attentive network. Subsequently, we thoroughly analyzed several backbone features to identify robust hyperparameters conducive to achieving our lightweight objective. Furthermore, we employed SA blocks to fortify the network, directing attention efficiently towards diseased regions. The efficacy of the proposed network is validated through comprehensive evaluations conducted on both our proposed and LWDCD2020 benchmarks. Comparative analyses with existing methods consistently showcase superiority, firmly establishing our proposed approach as a viable network for wheat disease recognition. additionally, we also collected our own dataset, namely the Wheat Disease Five Classes Classification Dataset (WD5CC), and included diverse images.

Keywords

• Image processing
• intelligent system
• smart agriculture
• lightweight network
• Internet of Things
• deep learning