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

Wei Li (), Lizhou Zhu and Jun Liu
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Wei Li: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Lizhou Zhu: College of Software Engineering, Southeast University, Suzhou 215123, China
Jun Liu: Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China

Agriculture, 2024, vol. 14, issue 5, 1-14

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.

Keywords: leaf disease detection; PL-DINO; convolutional block attention module; equalization loss; crop (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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