Comparison of RSNET model with existing models for potato leaf disease detection

Singh, Gulbir; Yogi, Kuldeep Kumar

doi:10.1016/j.bcab.2023.102726

Cited by 20 publications

(1 citation statement)

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“…The convolutional neural network (CNN) model was developed to categorize plant leaf disease using various frameworks [11]- [15]. MobileNet and visual geometry group 19 (VGG19) were used to compare and investigate the effects of the proposed approach in the initial procedure, among other things.…”

Section: Introductionmentioning

confidence: 99%

Classification of tea leaf disease using convolutional neural network approach

Hairah,

Septiarini,

Puspitasari

et al. 2024

IJECE

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Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.

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Section: Introductionmentioning

confidence: 99%