An Automatic Detection of Citrus Fruits and Leaves Diseases Using Enhanced Convolutional Neural Network

Shastri, Rajveer; Chaturvedi, Abhay; Mouleswararao, B.; Varalakshmi, Sudha; Prasad, G. N. R.; Ram, Mylavarapu Kalyan

doi:10.1007/s41976-023-00086-9

Cited by 5 publications

(1 citation statement)

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“…In [14], an innovative method for precise and automatic disease detection by employing the CNNs method was developed. This developed Enhanced-CNN (E-CNN) framework must be trained employing multiple collections of 3 benchmark databases namely the citrus leaf dataset, citrus pest and disease database, and citrus fruits and leaves database.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%