Hunger games search based deep convolutional neural network for crop pest identification and classification with transfer learning

Sanghavi, Vishakha B.; Bhadka, Harshad; Dubey, Vijay

doi:10.1007/s12530-022-09449-x

Cited by 10 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DenseNet 121 Detection and classification ACC: 88.06% -99.1% (Abbas et al, 2021), (Sanghavi et al, 2022), (Zhang & Chen, 2020), (Shi et al, 2020) DenseNet 169 Detection mAP: 92.3% (Butera et al, 2021) DenseNet 201 Detection and classification ACC: 79.01%, 95.52% (Nanni et al, 2022), (Singh et al, 2021) Weakly DenseNet-16 Classification ACC: 93.42% (Xing et al, 2019) EfficientNet 4…”

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

confidence: 99%

“…EfficientNet Detection ACC: 97.89% -99.1% (Dai et al, 2021), (Sanghavi et al, 2022), (Takimoto et al, 2021) EfficientNet B0 Detection ACC: 94.25% (Nanni et al, 2022) EfficientDet 1…”

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

confidence: 99%

“…Classification ACC: 75.3% -99.04% mAP: 71% (Ayan et al, 2020), (Fang et al, 2020), (Hansen et al, 2019), (Rajeena et al, 2022), (Sanghavi et al, 2022), (Singh et al, 2021), , (Liu et al, 2022) Inception ResNetv2 Detection ACC: 91.14% (Khanramaki et al, 2021), (Singh et al, 2021) LeNet et al, 2021), (Hong et al, 2021), (Nanni et al, 2022), (Rajeena et al, 2022), (Xing et al, 2019) (Ahmad et al, 2021), (Alsanea et al, 2022), (Butera et al, 2021), (Du et al, 2022), (Guo et al, 2021), (Hong et al, 2021), (Li et al, 2019), (Liu et al, 2019), (Wang et al, 2022), (Shi et (Butera et al, 2021), (De Cesaro Juńior et al, 2022), (Fang et al, 2020), (Dai et al, 2021), (Khanramaki et al, 2021), (Li et al, 2019), (Liu et al, 2019), (Liu et al, 2022), (Malathi and Gopinath, 2021), (Nanni et al, 2022), (Rajeena et al, 2022), (Sanghavi et al, 2022), , (Wang et al, 2022), (Xu et al, 2022) ResNet 53 Detection mAP: 77.29% (Lv et al, 2022) ResNet 101 Detection mAP: 85.53% -99.5%…”

Section: Inception V3mentioning

confidence: 99%

“…Harmful insect detection (Albanese et al, 2021), (Alsanea et al, 2022), (Ayan et al, 2020), (Butera et al, 2021), (Cochero et al, 2022), (Genaev et al, 2022), (Guo et al, 2021), (Hansen et al, 2019), (Hong et al, 2021), (Hossain et al, 2019), (Iost Filho et al, 2022), (Espinoza et al, 2016), (Kasinathan et al, 2021), (Khanramaki et al, 2021), (Knyshov et al, 2021), (Li et al, 2019), (Li et al, 2020), , (Liu et al, 2019), (Liu and Wang, 2020), (Lv et al, 2022), (Malathi and Gopinath, 2021), (Nagar and Sharma, 2021), (Nanni et al, 2022), (Rajeena et al, 2022), (Rimal et al, 2022), (Rustia et al, 2020), (Sanghavi et al, 2022), (Teng et al, 2022), (Valan et al, 2019), , (Wang et al, 2022), (Xia et al, 2018), (Zhang & Chen, 2020), (Shi et al, 2020) Infected crops by insects (Bereciartua-Peŕez et al, 2022), (Bhoi et al, 2021), (Fang et al, 2020), (Espinoza et al, 2016), (Kusrini et al, 2021), (Nazri et al, 2018), (Sharma et al, 2020), (Singh et al, 2021),…”

Section: Application Papersmentioning

confidence: 99%

“…The best ensemble, combining CNNs with various Adam variants, achieved impressive results, surpassing human expert classifications on several known datasets. With the awareness that agricultural pests severely impair food crop quality, the importance of agriculture as an economic backbone is underlined in (Sanghavi et al, 2022). Machine learning models have been employed to handle pest categorization and detection, however they suffer when dealing with insects that have similar traits but live in diverse environments.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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.

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

“…EfficientNet Detection ACC: 97.89% -99.1% (Dai et al, 2021), (Sanghavi et al, 2022), (Takimoto et al, 2021) EfficientNet B0 Detection ACC: 94.25% (Nanni et al, 2022) EfficientDet 1…”

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

confidence: 99%

Section: Inception V3mentioning

confidence: 99%

Section: Application Papersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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.

View full text Add to dashboard Cite

show abstract

Segmentation and detection of crop pests using novel U‐Net with hybrid deep learning mechanism

Biradar,

Hosalli

2024

Pest Management Science

View full text Add to dashboard Cite

In India, agriculture is the backbone of economic sectors because of the increasing demand for agricultural products. However, agricultural production has been affected due to the presence of pests in the crops. Several methods were developed to solve the crop pest detection issue, but they failed to achieve better results. Therefore, the proposed study used a new hybrid deep learning mechanism for segmenting and detecting pests in crops. Image collection, pre‐processing, segmentation, and detection are the steps involved in the proposed study. There are three steps involved in pre‐processing: image rescaling, equalized joint histogram based contrast enhancement (Eq‐JH‐CE), and bendlet transform based De‐noising (BT‐D). Next, the pre‐processed images are segmented using the DenseNet‐77 UNet model. In this section, the complexity of the conventional UNet model is mitigated by hybridizing it with the DenseNet‐77 model. Once the segmentation is done with an improved model, the crop pests are detected and classified by proposing a novel Convolutional Slice‐Attention based Gated Recurrent Unit (CS‐AGRU) model. The proposed model is the combination of a convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). In order to achieve better accuracy outcomes, the proposed study hybridized these models due to their great efficiency. Also, the slice attention mechanism is applied over the proposed model for fetching relevant feature information and thereby enhancing the computational efficiency. So, pests in the crop are finally detected using the proposed method. The Python programming language is utilized for implementation. The proposed approach shows a better accuracy range of 99.52%, IoU of 99.1%, precision of 98.88%, recall of 99.53%, F1‐score of 99.35%, and FNR of 0.011 compared to existing techniques.This article is protected by copyright. All rights reserved.

show abstract

An effective segmentation and attention‐based reptile residual capsule auto encoder for pest classification

Biradar,

Hosalli

2024

Pest Management Science

View full text Add to dashboard Cite

Insect pests are a major global factor affecting agricultural crop productivity and quality. Rapid and precise insect pest detection is crucial for improving handling and prediction techniques. There are several methods for pest detection and classification tasks; still, the inaccurate detection, computation complexity and several other challenges affect the performance of the model. Thus, this research presents a Deep Learning (DL) approach that has led to significant advancements and is currently being applied successfully in many domains, such as autonomous insect pest detection. Initially, the input images are gathered from the test dataset. The next step in pre‐processing the input images is to improve the model capacity by removing unwanted data using the Enhanced Kuan filter method. Then, the pre‐processed images are segmented using the Attention‐based U‐Net method. Finally, a novel Attention Based Reptile Residual Capsule Auto Encoder (ARRCAE) technique is proposed to classify and recognize crop pests. Furthermore, the Improved Reptile Search Optimisation (IRSO) algorithm is employed to fine‐tune the classification parameters optimally. As a result, the proposed study enhances performance by classifying crop pest detection systems. The suggested method makes use of a Python tool for simulation, and pest datasets are utilized for result analysis. The suggested model beats other current models with an accuracy of 98%, precision of 97%, recall of 96%, and specificity of 99% for the pest dataset, per the simulation results that were obtained.This article is protected by copyright. All rights reserved.

show abstract

Hunger games search based deep convolutional neural network for crop pest identification and classification with transfer learning

Cited by 10 publications

References 42 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

Segmentation and detection of crop pests using novel U‐Net with hybrid deep learning mechanism

An effective segmentation and attention‐based reptile residual capsule auto encoder for pest classification

Contact Info

Product

Resources

About