PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection

Abstract

Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.

Plant phenotyping relevance

植物葉画像から病害状態を検出する画像解析手法を開発・評価しており、植物の病害表現型推定が中心です。

Code and data availability

The paper uses the public PlantDoc dataset, but that is a cited third-party benchmark, not a paper-specific asset. No author code, models, or supplementary data are deposited; the Data Availability Statement only offers data from the corresponding author on request, with no public URL.

No evidence-backed public reproduction asset is currently recorded.