A Review on Image Processing Algorithm for Foliage Target Detection and Classification

Bhavsar, Nakshtra; Tayade, Akanksha; Ghodmare, Hrutesh; Wagdharikar, N.M.; De, Sampurna; Raj, A.A Bazil

doi:10.5281/zenodo.7486512

Cited by 1 publication

(1 citation statement)

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“…Traditional machine learning models, such as support vector machines (SVM), decision trees, K-means, and random forests, require manual feature design specific to different disease types, making them susceptible to environmental factors and lacking generalization capabilities ( Bhavsar et al., 2022 ; Zou et al., 2020 ; Steven, 2021 ; Yu et al., 2021 ; Bao et al., 2022 ; Prabu et al., 2022 ). Conversely, deep learning, particularly in object detection, exhibits potential in crop disease identification ( Krisnandi et al., 2019 ; Ayan et al., 2020 ; Jiang et al., 2020 ; Tetila et al., 2020 ; Xiong et al., 2020 ; Hu et al., 2021b ).…”

Section: Introductionmentioning

confidence: 99%

TeaDiseaseNet: multi-scale self-attentive tea disease detection

Sun,

Wu,

Guo

et al. 2023

Front. Plant Sci.

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Accurate detection of tea diseases is essential for optimizing tea yield and quality, improving production, and minimizing economic losses. In this paper, we introduce TeaDiseaseNet, a novel disease detection method designed to address the challenges in tea disease detection, such as variability in disease scales and dense, obscuring disease patterns. TeaDiseaseNet utilizes a multi-scale self-attention mechanism to enhance disease detection performance. Specifically, it incorporates a CNN-based module for extracting features at multiple scales, effectively capturing localized information such as texture and edges. This approach enables a comprehensive representation of tea images. Additionally, a self-attention module captures global dependencies among pixels, facilitating effective interaction between global information and local features. Furthermore, we integrate a channel attention mechanism, which selectively weighs and combines the multi-scale features, eliminating redundant information and enabling precise localization and recognition of tea disease information across diverse scales and complex backgrounds. Extensive comparative experiments and ablation studies validate the effectiveness of the proposed method, demonstrating superior detection results in scenarios characterized by complex backgrounds and varying disease scales. The presented method provides valuable insights for intelligent tea disease diagnosis, with significant potential for improving tea disease management and production.

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