CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile

Lanjewar, Madhusudan G.; Parab, Jivan S.

doi:10.1007/s11042-023-16886-6

Cited by 11 publications

(5 citation statements)

References 61 publications

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“…Currently, citrus disease classification is commonly performed by convolutional neural networks (CNNs) since the data representations are automatically learned by transforming the input image into multiple layers of abstractions [8] , [9] , [10] , [11] . CNN architectures usually have millions of trainable parameters, making CNNs prone to overfitting when training from scratch using a small dataset.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Artificial data augmentation increases the quantity of data by randomly applying image transformations to the training set [11] . This implicit regularization reduces overfitting because it is unlikely to generate the same image twice, enabling invariance and adding an extra source of stochasticity to CNN learning [13] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

See 1 more Smart Citation

CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques

Gómez-Flores,

Garza-Saldaña,

Varela-Fuentes

2024

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques

Gómez-Flores,

Garza-Saldaña,

Varela-Fuentes

2024

Data in Brief

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“…The authors in [17], aimed to recognize normal and diseased leaf images employing a CNN under the Platform as a Service (PaaS) cloud. The database of 5 categories of normal and diseased images has been implemented.…”

Section: Related Workmentioning

confidence: 99%

“…𝜏 𝑚 𝑑𝑣 𝑑𝑡 = 𝑎 + 𝑅 𝑚 𝐼 − 𝜈; 𝜈 = 𝜈 𝑟𝑒𝑠𝑒𝑡 , 𝑖𝑓 𝜈 > 𝑣 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 (17) Where 𝑎 denotes the resting potential. 𝜏 𝑚 = 𝑅 𝑚 𝐶 𝑚 is time constant, 𝐶 𝑚 is membrane capacitance and 𝑅 𝑚 is membrane resistance.…”

Section: Classification Using Rsnn Modelmentioning

confidence: 99%

Internet of Things Assisted Plant Disease Detection and Crop Management using Deep Learning for Sustainable Agriculture

Al-Shahari,

Aldehim,

Aljebreen

et al. 2024

IEEE Access

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The integration of Internet of Things (IoT) technology with deep learning (DL) algorithms has revolutionized plant disease detection and crop management and paved the way for sustainable agricultural practices. Real-time information on soil moisture, plant health, and environmental conditions can be collected by deploying a network of connected devices and sensors in agricultural fields. DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. This early detection allows farmers to implement targeted interventions, like adjustment to irrigation or precision application of pesticides, maximizing crop yield, and minimizing resource wastage. Therefore, this article develops an automated Plant Disease Detection and Crop Management using a spotted hyena optimizer with deep learning (APDDCM-SHODL) technique for Sustainable Agriculture. The APDDCM-SHODL approach aims to detect the existence of plant diseases and improve crop productivity in the IoT infrastructure. To achieve this, the APDDCM-SHODL method primarily employs the Vector Median Filter (VMF) technique. In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. A brief set of experiments has been made to determine the experimental validation of the APDDCM-SHODL model. The comprehensive results inferred that the APDDCM-SHODL method reaches remarkable performance over other existing methods with the highest accuracy of 98.60%.

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“…It provides insights into the concepts, applications, and theories driving advancements in this field, with detailed discussions on various outcomes achieved by these methodologies. In [ 36 ] author uses different deep learning models of CNN with augmentation to classify the infected and healthy leaf of the citrus plant and deploy it to the platform as a service (PaaS). The dataset was divided into five class one for healthy leaves and the other 4 are of unhealthy leaves.…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture

Shahid,

Khanzada,

Aslam

et al. 2024

Plant Methods

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Background Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country’s economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. Methods To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. Results During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. Conclusion The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.

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CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile

Cited by 11 publications

References 61 publications

CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques

CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques

Internet of Things Assisted Plant Disease Detection and Crop Management using Deep Learning for Sustainable Agriculture

An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture

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