Potato Blight: Deep Learning Model for Binary and Multi-Classification

Kukreja, Vinay; Baliyan, Anupam; Salonki, Vikas; Kaushal, Rajesh Kumar

doi:10.1109/spin52536.2021.9566079

Cited by 69 publications

(5 citation statements)

References 9 publications

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“…This research underscores the www.ijacsa.thesai.org effectiveness of InceptionResNetV2 as a robust model for accurate and reliable plant disease classification. Kukreja et al [11], the authors addressed the challenge of potato disease classification, specifically focusing on Potato Early Blight and Late Blight, their approach employed a simple CNN based deep learning model. Through their research, they achieved a notable accuracy rate of 94.77%.…”

Section: Related Workmentioning

confidence: 99%

“…The models used by Asif et al [8] and Sinshaw et al [13] demonstrated varying levels of accuracy, indicating a lack of consistency in their performance. Furthermore, the models employed by Naveenkumar et al [10] and Kukreja et al [11] were trained on proprietary datasets, which may limit the reproducibility of their results. Our proposed ensemble machine learning model addresses these limitations by leveraging the strengths of multiple pre-trained models and fine-tuning them on our extensive dataset.…”

Section: Related Workmentioning

confidence: 99%

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Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for Detection of Vegetable Crops Disease

Jha,

Dembla,

Dubey

2024

IJACSA

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In India, plant diseases pose a significant threat to food security, requiring precise detection and management protocols to minimize potential damage. Research introduces an innovative ensemble machine learning model for precise disease detection in tomato, potato, and bell pepper crops. Utilizing transfer learning, pre-trained models such as MobileNet and Inception are fine-tuned on a dataset of over 10,403 images of diseased and healthy plant leaves. The models are combined into a diverse ensemble, enhancing the precision and robustness of disease detection. The proposed ensemble models achieve an impressive accuracy rate of 98.95%, demonstrating their superiority over individual models in reducing misclassification and false positives. This advancement in plant disease detection provides valuable support to farmers and agricultural experts by enabling early disease identification and intervention.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for Detection of Vegetable Crops Disease

Jha,

Dembla,

Dubey

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…According to their findings, this CNN architecture achieved a testing accuracy of 99.95 ± 0.03 with 95% confidence interval for identifying diseased tomato plant leaves. Kukreja et al (2021) built a custom CNN model to diagnose disease in potato plants. Their proposed CNN model achieved a testing accuracy of 90.77% in detecting diseased potato plant leaves.…”

Section: Research Work Focused On Plant Disease Detectionmentioning

confidence: 99%

PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning

Bedi,

Gole,

Marwaha

2024

Front. Plant Sci.

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Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework’s performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework’s performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework’s applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.

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“…All nodes are believed to have fixed positions and the same initial energy. Furthermore, all nodes have a GPS and their positions have always been known [31][32][33][34].…”

Section: Proposed System Modelmentioning

confidence: 99%

REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks

Dogra,

Rani,

Gianini

2023

Energies

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An essential component of the Internet of Things (IoT) is wireless sensor networks (WSNs). Since individual sensor nodes are strongly power-constrained, several techniques are adopted to save power. By grouping nodes into clusters—thus reducing the transmission distance between sensor nodes and the base station (BS)—a clustering protocol can ensure energy preservation and increase the lifetime of the network. However, current clustering techniques have problems with the clustering structure that negatively impact their performance. Whenever routing protocols were implemented for a longer period of time, it was observed that they had a higher rate of energy consumption, a shorter period of stability, and fewer data transfers to the BS. In this paper, an improved region-based routing protocol (REERP) is developed for wireless sensor networks in the IoT is developed. It is based on (i) the addition of new nodes to the already formed clusters, (ii) the selection of the new head node based on the amount of residual energy, (iii) the setup of the multi-hop communication in all the regions of network, and (iv) the utilization of the energy hole reduction method. All of these tactics increase the useful life of the network. Performance has been evaluated against (1) a stable election protocol, (2) a gateway energy-aware routing protocol, and (3) a heterogeneous gateway energy-aware routing protocol, and using the metrics lifetime, energy consumption, number of dead nodes, and number of packets sent to the base station vs. number of packets acquired by the base station. The results of the proposed routing protocol have been found to outperform the state-of-the-art approaches considered.

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Potato Blight: Deep Learning Model for Binary and Multi-Classification

Cited by 69 publications

References 9 publications

Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for Detection of Vegetable Crops Disease

Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for Detection of Vegetable Crops Disease

PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning

REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks

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