Classification of Agricultural Leaf Images using Hybrid Combination of Activation Functions

Abhilasa, S; Srilakshmi, A.; Geetha, K.

doi:10.1109/iciccs51141.2021.9432221

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…To improve the accuracy of detection models, Abhilasa et al [18] proposed a combination of hybrid activation functions to improve the accuracy of CNN models. The hybrid activation function is tested and trained on different data sets, and the results show that this function has higher accuracy than ReLU activation function.…”

Section: Related Workmentioning

confidence: 99%

MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

Li,

Zhao,

Liu

2023

Electronics

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As the principal factor affecting global food production, accurate identification of agricultural pests and diseases is crucial in ensuring a sustainable food supply. However, existing methods lack sufficient performance in terms of accuracy and real-time detection of multiple pests and diseases. Accordingly, accurate, efficient, and real-time identification of a wide range of pests and diseases is challenging. To address this, we propose an MCD-Yolov5 with a fusion design that combines multi-layer feature fusion (MLFF), convolutional block attention module CBAM, and detection transformer (DETF). In this model, we optimize the MLFF design to realize the dynamic adjustment of feature weights of the input feature layer to (1) find an appropriate distribution of feature information proportion for the detection task, (2) enhance detection speed by efficiently extracting effective images and effective features through CBAM, and (3) improve feature extraction capability through DETF to compensate for the accuracy problem of multiple pest detection. In addition, we established an unmanned aerial vehicle system (UAV) for crop pest and disease detection to assist in detection and prevention. We validate the performance of the proposed method through an established UAV platform, and five indicators are employed to quantify the performance. MCD-Yolov5 can detect pests and diseases with a large improvement in detection accuracy and detection efficiency, obtaining an 88.12% accuracy. The proposed method and system provide an idea for the effective identification of pests and diseases.

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Section: Related Workmentioning

confidence: 99%

MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

Li,

Zhao,

Liu

2023

Electronics

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“…Many plant leaves from open database and manually processed dataset have been used in many works. A simple CNN can be modified by applying hybrid combination of activation functions for agriculture crop leaf disease detection, where activation functions like Rectified Linear Unit (ReLU), Gaussian Exponential Linear Unit (GeLU), Scaled Exponential Linear Unit (SeLU) can be used [10]. In some studies, a combination of VGG-16 and MobileNet deep learning models with stacking ensemble learning techniques are introduced to obtain 89% accuracy on sunflower leaves www.ijacsa.thesai.org [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

2024

IJACSA

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Maize and Paddy are pivotal crops in India, playing a vital role in ensuring food security. Timely detection of diseases and the implementation of remedial measures are crucial for securing optimal crop yield and profitability for farmers. This study utilizes a dataset encompassing images of diseased maize and paddy leaves, addressing various conditions such as corn blight, common rust, gray leaf spot, brown spot, hispa, and leaf blast, alongside images of healthy leaves. The dataset used here is a combination of online repository as well as manually collected samples from neighborhood farmlands at different growth stages. A machine vision approach that is accessible, quick, robust and cost effective to determine crop leaf diseases is need of the hour. In the proposed work, using transfer-learning approach, many Deep Convolutional Neural Networks (DCNN) and hybrid DCNNs have been developed, trained, validated and tested. To achieve better accuracy, integration of DCNNs and machine learning classifiers like multiclass Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms is carried out. The research is carried out in four stages, in the first stage, DCNNs have been used as classifiers. Subsequently, these same DCNNs are repurposed as feature extractors, and the extracted features are input into classifiers such as multiclass SVM and KNN. In the third stage, an ensemble of DCNNs is performed for networks exhibiting excellent performance during first stage. At a fourth stage, features extracted from these ensemble networks are fed into the same multiclass SVM and KNN classifiers to assess accuracy. A total of 1600 images for training and 400 images for testing are used. For maize data set, we achieved a 100% accuracy in AlexNet plus VGG-16 hybrid network for multiclass SVM with 75:25 split ratio and for paddy dataset 99.51% accuracy is achieved in ResNet-50 plus Darknet-53 hybrid network for multiclass SVM with 75:25 split ratio. In the proposed study a comprehensive analysis is conducted, exploring features from various layers and adjusting data split ratios.

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A Comparative Analysis on Various Modified Deep Convolution Neural Networks on Maize Plant Leaf Disease Classification

Kumar,

Poornima

2024

Lecture Notes in Electrical Engineering

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Classification of Agricultural Leaf Images using Hybrid Combination of Activation Functions

Cited by 3 publications

References 19 publications

MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

A Comparative Analysis on Various Modified Deep Convolution Neural Networks on Maize Plant Leaf Disease Classification

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