A Novel Salp Swarm Algorithm With Attention-Densenet Enabled Plant Leaf Disease Detection And Classification In Precision Agriculture

Devi, S. Nirmala; Muthukumaravel, A.

doi:10.1109/icacta54488.2022.9753001

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…A maximum of 94% classification accuracy is obtained with the random forest method [30]. Another attentionbased method proposed by Devi et al [48] that used the Salp Swarm Algorithm had 97.56% accuracy to predict five types of tomato leaf disease. The Lightweight Attention-Based CNN mechanism [49] to classify ten types of tomato leaf disease.…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

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Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification

Islam

Sultana

Farid

et al. 2022

Sensors

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Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.

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Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…Another attention-based method proposed by Devi et al [48] that used the Salp Swarm Algorithm to classify tomato leaf disease. This method got 97.56% accuracy to predict five types of tomato leaf disease from plan village data.…”

Section: Deep Learning With Machine Learningmentioning

confidence: 99%

Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification

Islam

Sultana

Farid

et al. 2022

Sensors

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“…In 2022, an attention-based method was proposed by Devi et al [ 47 ] where they used the Salp Swarm algorithm in the classification of tomato leaf diseases. Their method achieved an accuracy of 97.56% in predicting five types of tomato leaf diseases from leaf images taken from the plant village dataset.…”

Section: Related Workmentioning

confidence: 99%

“… Benchmark against other models [ 27 , 47 , 48 , 49 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]. …”

Section: Figurementioning

confidence: 99%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

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Retracted: Plant Disease Classification for Precision Agriculture Applications in Resource Constrained Environments

Mehta

Pant

2022

2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)

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A Novel Salp Swarm Algorithm With Attention-Densenet Enabled Plant Leaf Disease Detection And Classification In Precision Agriculture

Cited by 5 publications

References 27 publications

Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification

Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Retracted: Plant Disease Classification for Precision Agriculture Applications in Resource Constrained Environments

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