Classification of Plant Leaves Using New Compact Convolutional Neural Network Models

Wagle, Shivali Amit; Ali, Sawal Hamid Md; Faseehuddin, Mohammad

doi:10.3390/plants11010024

Cited by 30 publications

(26 citation statements)

References 50 publications

(69 reference statements)

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“…To enhance the VGG-19 model, they incorporated the inception module, which resulted in an improved performance. In [28], Wagle et al suggested a compact CNNbased framework for leaf infection classification. Their compact CNN-based architecture utilized "Rectified Linear Unit" ReLu [46] activation functions throughout the network and fully connected layer before making a final prediction, which increased the total training parameters and overall model complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, with the advent of domain-specific architectures [18] (i.e., GPU, TPU, etc. ), deep learning (DL)-based classifiers, particularly CNN, are becoming increasingly popular [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. A CNN-based classifier (i.e., VGG-Net [34], ImageNet [35], Inception-Net [36], DenseNet-121 [37], MobileNet [38], ResNet50 [39], etc.)…”

Section: Introductionmentioning

confidence: 99%

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PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Alghamdi

Turki

2023

Agriculture

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Overlooked diseases in agriculture severely impact crop growth, which results in significant losses for farmers. Unfortunately, manual field visits for plant disease diagnosis (PDD) are costly and time consuming. Although various methods of PDD have been proposed, many challenges have yet to be investigated, such as early stage leaf disease diagnosis, class variations in diseases, cluttered backgrounds, and computational complexity of the diagnosis system. In this paper, we propose a Convolutional Neural Network (CNN)-based PDD framework (i.e., PDD-Net), which employs data augmentation techniques and incorporates multilevel and multiscale features to create a class and scale-invariant architecture. The Flatten-T Swish (FTS) activation function is utilized to prevent gradient vanishing and exploding problems, while the focal loss function is used to mitigate the impact of class imbalance during PDD-Net training. The PDD-Net method outperforms baseline models, achieving an average precision of 92.06%, average recall of 92.71%, average F1 score of 92.36%, and accuracy of 93.79% on the PlantVillage dataset. It also achieves an average precision of 86.41%, average recall of 85.77%, average F1 score of 86.02%, and accuracy of 86.98% on the cassava leaf disease dataset. These results demonstrate the efficiency and robustness of PDD-Net in plant disease diagnosis.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Alghamdi

Turki

2023

Agriculture

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“…The accuracy rate of the study using Flavia data was 93.82%. Wagle et al [32] designed a compact CNN with different convolutional layers, named N1, N2, and N3, for plant species classification. They tested this compact CNN model on the PlantVillage and Flavia datasets.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Aslan

2023

Balkan Journal of Electrical and Computer Engineering

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In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural Networks (CNN) methods. In the proposed method, first, image pre-processing is performed to increase the accuracy of the posterior process. The obtained image is a grayscale image without noise as a result of the pre-processing. These preprocessed images are used in classification with ML and DL. The Speeded Up Robust Features (SURF) are extracted from the grayscale image for ML-based learning. The features are restructured as visual words using the Bag of Visual Words (BoVW) method. Then, histograms are generated for each image according to the frequency of the visual word. Those histograms represent the new feature data. The histogram features are classified by four different ML methods, Decision Tree (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Before using the ML methods, Bayesian Optimization (BO) method, which is one of the Hyperparameter Optimization (HO) algorithms, is applied to determine hyperparameters. In the classification process performed with four different ML algorithms, the best accuracy is achieved with the KNN algorithm as 98.09%. Resnet18, ResNet50, MobileNet, GoogLeNet, DenseNet, which are state-of-the-art CNN architectures, are used for DL-based learning. CNN models have higher accuracy than ML algorithms.

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“…In recent years, convolutional-neural-network-based (CNN) deep learning approaches have rapidly developed. Compared with conventional methods, CNNs extract different levels of features from input images, achieving accurate target detection through information classification and location regression [ 30 , 31 ]. The well-known CNN architectures, such as AlexNet, ResNet50, and VGG16, can be utilized for the classification and diagnosis of diseases as well as the quality inspection of fruits and vegetables [ 32 , 33 , 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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Classification of Plant Leaves Using New Compact Convolutional Neural Network Models

Cited by 30 publications

References 50 publications

PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

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