A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation

Zhang, Liankuan; Xia, Chunlei; Xiao, Di; Weckler, Paul; Lan, Yubin; Lee, Jang M.

doi:10.1016/j.biosystemseng.2021.03.017

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…[20] Regressão Logística (RL) [21] entrada [5], [6], [7], [8]. As CNNs apresentam bom desempenho com acurácia acima de 99%, resultado obtido em outro conjunto de dados, o qual consiste em um estudo de detecção automática em um sistema de visão para avaliação da qualidade da limpeza robótica de plantas de processamento de pescado [9].…”

Section: Algoritmounclassified

Classificação de Folhas Nativas do Cerrado Usando Aprendizado de Máquina

Faria¹,

Bambil²,

Café³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Este trabalho compara 8 algoritmos de aprendizado de máquina em função da sua acurácia média de classificação de 30 espécies de plantas. O sistema de classificação utiliza fotos de folhas como entrada para o sistema de classificação. Os resultados evidenciam que o algoritmo Análise de Discriminante Linear se mostrou o mais apropriado para este problema específico, chegando a 98,46% de acurácia média.

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Section: Algoritmounclassified

Classificação de Folhas Nativas do Cerrado Usando Aprendizado de Máquina

Faria¹,

Bambil²,

Café³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…Singh and Sabrol proposed CNN based model for crop disease identification [28]. Zhang et al proposed a deep learning based model known as R-CNN comprising of joint segmentation and leaf skeleton identification [29]. Tavakoli et al [30].…”

Section: Deep Cnn Modelsmentioning

confidence: 99%

Plant Disease Detection using Advanced Convolutional Neural Networks with Region of Interest Awareness

T¹,

K²

2022

J Immuno Res & Reports

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The advent of deep learning has paved way for more efficient computer vision applications. In agricultural crop monitoring with technology driven approaches, it is indispensable to have plant disease identification. Recent research reveals that Convolutional Neural Network (CNN) is most suitable deep learning method to process leaf images for detecting diseases. As symptoms of leaf disease appear in specific area, considering entire leaf for processing incurs more computational cost and time besides deteriorating performance due to inadequate quality of training. To overcome this problem, we proposed a framework that considers extraction of ROI using deep CNN prior to prediction of pre-trained deep learning models such as VGG13, Res Net34, Dense Net19, Alex Net, Sqeezenet1_1 and Inception_v3. An algorithm named ROI Feature Map Creation (ROI-FMC) is defined to extract ROI for given input image. This will be given as input to another algorithm proposed namely ROI based Deep CNN with Transfer Learning for Leaf Disease Prediction (ROIDCNN-LDP). The latter is used to predict leaf diseases. Plant Village dataset is used for empirical study. The experimental results revealed that with ROI awareness, all models could perform well. However, Inception_v3 is the deep CNN model that outperforms other models

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“…Among the various types of food crops, rice or paddy is mainly used by many people in different countries. Paddy leaves are frequently affected by some diseases such as “leaf streak, leaf brown spot, leaf blast, false smut, leaf blight, leaf scald, red stripe etc.” These diseases are majorly caused by viruses, bacteria, and fungi that reduce the yield of paddy or rice (Zhang, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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Paddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases.Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by "adaptive K-means clustering." This is also accomplished by the Fitness Sorted-Shark Smell Optimization (FS-SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS-SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

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A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation

Cited by 12 publications

References 34 publications

Classificação de Folhas Nativas do Cerrado Usando Aprendizado de Máquina

Classificação de Folhas Nativas do Cerrado Usando Aprendizado de Máquina

Plant Disease Detection using Advanced Convolutional Neural Networks with Region of Interest Awareness

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

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