Neural Network-based Pest Detection with K-Means Segmentation: Impact of Improved Dragonfly Algorithm

Chodey, Madhuri Devi; C, Noorullah Shariff

doi:10.1142/s0219649221500404

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…CapsNet/modified Classification ACC: 82.4%, PRE: 75.41% (Xu et al, 2022), CNN 8 CNN Classification ACC: 91.5% -98,6% F1 score: 95% (Chodey & Shariff, 2021), (Hossain et al, 2019), (Espinoza et al, 2016), (Kasinathan et al, 2021), (Sharma et al, 2020), (Singh et al, 2021) BPNN Classification ACC: 91% (Zhu et al, 2020) DenseNet 8…”

Section: Neural Network Used In Insect Detection Segmentation and Cla...mentioning

confidence: 99%

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Popescu,

Dinca,

Ichim

et al. 2023

Front. Plant Sci.

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Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production.

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Section: Neural Network Used In Insect Detection Segmentation and Cla...mentioning

confidence: 99%

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Popescu,

Dinca,

Ichim

et al. 2023

Front. Plant Sci.

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show abstract

“…The CIFAR-10 dataset includes 60000 different images from a total of 10 categories. Due to the large sample size, the experiment will randomly select 1500 images from each category as the training dataset, while the test dataset includes 2500 randomly selected images from the entire image database [7].…”

Section: Introduction To Experimental Datasetsmentioning

confidence: 99%

A computer image feature extraction algorithm based on distributed K-means algorithm

Dai,

Ling,

Xie

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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In order to understand computer image feature extraction algorithms, a research on computer image feature extraction algorithm based on distributed K-means algorithm has been proposed. The feature extraction of images, as the key, fundamentally determines the accuracy of image recognition. With the advent of the big data era, feature learning methods represented by deep learning models have demonstrated their powerful capabilities in image feature extraction tasks. Secondly, through layer by layer training, deep learning models can extract deep features of images in a highly autonomous manner, and have achieved the best results in the current research stage. Although deep learning models have extremely powerful learning abilities, the complexity of their model structure and the overhead involved in training also hinder their training. Based on clustering algorithms, a hierarchical image feature extraction model is constructed to improve the accuracy of image classification.

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“…However, many agricultural personnel do not have a comprehensive knowledge of disease control, and when judging crop diseases, they often only observe, which inevitably produces errors, thus hindering the timely treatment of crops. With the continuous improvement of the level of computer hardware and the rapid growth of computing speed, pattern recognition and artificial intelligence have developed rapidly on this basis; image processing and machine learning technology has become more mature and has begun to enter people's production and life, providing great convenience for people's daily labour [2][3][4]. In recent years, there have been an increasing number of studies on crop disease identification.…”

Section: Introductionmentioning

confidence: 99%

Soybean Leaf Disease Detection

Bhujbal¹,

Mandale²,

Aher³

et al. 2023

IJRASET

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With the continuous integration of computer technology into agricultural production, it also reduces personnel costs while improving agricultural production efficiency and quality. Crop disease control is an important part of agricultural production, and the use of computer vision technology to quickly and accurately identify crop diseases is an important means of ensuring a good harvest of agricultural products and promoting agricultural modernization. In this paper, a recognition method based on deep learning is proposed based on soybean brown spot. The method is divided into image pre- treatment and disease identification. Based on traditional threshold segmentation, the pre-processing process first uses the HSI colour space to filter the information of the normal area of the leaf, adopts OTSU to set the threshold to segment the original image under the Lab colour space, and then merges the segmented images. The final spot segmentation image is obtained. Compared with the renderings of several other commonly used methods of segmentation, this method can better separate the lesions from the leaves. In terms of disease identification, in order to adapt to the working conditions of large samples of farmland operations, a convolutional neural network (CNN) of continuous convolutional layers was constructed with the help of Caffe to extract more advanced features of the image. In the selection of activation functions, this paper selects the Max out unit with stronger fitting ability, and in order to reduce the parameters in the network and prevent the network from overfitting, the sparse Max out unit is used, which effectively improves the performance of the Max out convolutional neural network. The experimental results show that the algorithm is superior to the algorithm based on ordinary convolutional neural network in identifying large sample crop diseases.

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Neural Network-based Pest Detection with K-Means Segmentation: Impact of Improved Dragonfly Algorithm

Cited by 11 publications

References 34 publications

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

A computer image feature extraction algorithm based on distributed K-means algorithm

Soybean Leaf Disease Detection

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