A New Dawn for Tomato-spotted wilt virus Detection and Intensity Classification: A CNN and LSTM Ensemble Model

Published: May 26, 2023

Tomato-spotted wilt virus (TSWV) is a severe plant disease that causes significant economic losses in tomato production worldwide. Early detection and intensity classification of TSWV-infected plants critical for effective management. This study proposes novel TSWV approach based on convolutional neural network (CNN) long short-term memory (LSTM) ensemble model. A dataset comprising 30,000 images infected with was gathered annotated six levels, ranging from 0 (indicating no symptoms) to 5 symptoms). framework developed, aiming enhancing the model’s performance r proposed achieved an overall accuracy 97.37% test set, outperforming several state-of-the-art approaches. We also performed statistical analysis inter-intensity level variability found increased level. Our results suggest has potential be used early plants, which could aid timely application preventive measures reduce caused by TSWV.

Language: Английский

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Revolutionizing Rice Farming: Automated Identification and Classification of Rice Leaf Blight Disease Using Deep Learning

Published: May 26, 2023

Language: Английский

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Fighting Grape Black Rot with Deep Learning: A CNN-LSTM Hybrid Model for Disease Severity Classification

Published: May 26, 2023

Grape black rot is a devastating disease that affects grape crops globally. Detecting and preventing the as early possible crucial for minimizing crop loss increasing yield quality. In this study, we propose CNN-LSTM hybrid model multi-classification of severity based on six distinct degrees. The obtained an accuracy 93.06% after being trained dataset 10,000 leaf images collected from Indian vineyard, outperforming all other deep learning (DL) traditional machine models. capacity proposed to capture both spatial temporal characteristics images, well application data augmentation techniques halting, contributed its superior performance. can be utilized efficient instrument detection prevention disease, thereby contributing enhancement quality crops. However, model's performance varied depending degree with reduced classification leaves severe degree. To enhance ability precisely classify rot, additional research required. Overall, promising approach potential applications plant tasks.

Language: Английский

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Deep Learning Meets Agriculture: A Faster RCNN Based Approach to pepper leaf blight disease Detection and Multi-Classification

Published: May 26, 2023

Pepper Leaf Blight Disease (PLBD) is a widespread plant ailment that has severe impact on pepper cultivation across the globe. The rapid detection and precise classification of PLBD severity levels are crucial for efficient disease control optimal agricultural productivity. present study introduces novel model based Faster region-based convolutional neural network (R-CNN) multi-classification in leaves. dataset used training testing consisted 10,000 images. model's performance was evaluated its accuracy accuracy, which were found to be 99.39% 98.38%, respectively. computational efficiency assessed determined sufficient deployment real-time applications. average inference time 0.23 seconds per image renders it appropriate high-throughput study's findings indicate faster RCNN successful method detecting classifying This potential enhance management crop yield farming.

Language: Английский

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A Smart Approach to Coconut Leaf Spot Disease Classification using Computer Vision and Deep Learning Technique

Published: July 14, 2023

Coconut leaf spot (CLS) disease is a major threat to coconut production and can cause severe economic losses. In this study, we propose deep learning (DL)-based ResNext50 model for automated detection severity classification of CLS disease. Our leverages mode; trained tested on dataset images with six levels, ranging from healthy leaves critical severity. The proposed approach achieves high accuracy in detecting classifying the levels findings suggest that method successful properly identifying categorizing illness an rate 91.77% overall. strategy has been presented possibility significantly improve efficiency monitoring, ultimately leading better management strategies increased productivity industry.

Language: Английский

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Sugar Learning: Deep Learning for Rapid Detection and Classification of Sugarcane Diseases

Published: July 14, 2023

Sugarcane is a widely cultivated crop due to its high demand and supply in various industries. However, the increase production levels has led an number of diseases that affect crop. One most devastating sugarcane red rot (SRR) disease. A multi-layer perceptron (MP) based deep learning (DL) model was built for identification classification SRR illness using dataset 20,000 photos leaves order address this problem. This trained on photographs leaves. The classified into 5 different disease severity levels. proposed achieved accuracy rate 97.97% binary 98.03% overall multi-classification. Furthermore, comparison stages carried out, it demonstrated effective tool accurately categorizing images study contributes development efficient accurate early detection diagnosis crops, which essential improving yield preventing economic losses.

Language: Английский

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Deep Learning Based Sugarcane Downy Mildew Disease Detection Using CNN-LSTM Ensemble Model for Severity Level Classification

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2023, Volume and Issue: unknown

Published: July 6, 2023

The fungus known as sugarcane downy mildew is extremely destructive and represents a substantial risk to output all over the world. To effectively manage disease safeguard crops, early precise diagnosis of severity levels caused by essential. In this study, we present novel method for detecting based on using CNN-LSTM ensemble model. model combines spatial feature extraction skills Convolutional Neural Networks (CNN) with temporal modeling abilities Long Short-Term Memory (LSTM) networks. For training assessment, dataset consisting leaf pictures that have been afflicted labeled ranging from 1 5 employed. results experiments show suggested successful, it achieves high accuracy, precision, recall, F1 score while attempting forecast mildew. capacity categorize throughout demonstrated overall accuracy 94.16%. was provided provides potential solution automated identification illness enables prompt interventions optimizes management practices. proposed study contributes growing field agricultural informatics helps promote environmentally responsible methods sugarcane.

Language: Английский

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Deep Learning Techniques for Pneumonia Detection on Chest X-Ray Images

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 152 - 167

Published: Jan. 1, 2025

Language: Английский

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DeepLeaf: Revolutionizing Rice Disease Detection and Classification using Convolutional Neural Networks and Random Forest Hybrid Model

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2023, Volume and Issue: unknown

Published: July 6, 2023

Rice hispa disease is a severe risk to agricultural production and can result in considerable reductions crop yield. It necessary, put into practice efficient management techniques, correctly categorize the its various degrees of intensity. In this study, we present hybrid model for multi-classification rice illness that incorporates convolutional neural networks (CNN) Random Forest (RF). A dataset consisting 10,000 photos, each which represents distinct degree intensity, was gathered pre-processed. The CNN component responsible extracting distinguishing characteristics from pictures, while RF classifier charge incorporating these final classification. displays competitive performance when compared classic machine learning (ML) techniques deep (DL) models, with an overall accuracy 97.46%. For certain measures, such as accuracy, recall, F1-score, are shown. confusion matrix analysis provides more evidence distinguish between different states. Our potential approach accurate reliable disease, paves way enhanced control production.

Language: Английский

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Mitigating Mustard Downy Mildew Disease: Early Detection and Prevention through a Hybrid CNN-SVM Model

Published: March 14, 2024

Language: Английский

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