PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction

Arshad, Farath; Mateen, Muhammad; Hayat, Shaukat; Wardah, Maryam; Al‐Huda, Zaid; Gu, Yeong Hyeon; Al-antari, Mugahed A.

doi:10.1016/j.aej.2023.07.076

Cited by 36 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fungal diseases in potato leaves can show different symptoms, depending on the causative organism. Early blight caused by Alternaria solani on leaves can be recognized as circular patterns manifested along leaf edges [4] , slightly sunken leaf spots with yellow borders, and concentric rings. In certain cases, these spots may converge.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

A novel dataset of potato leaf disease in uncontrolled environment

Shabrina,

Indarti,

Maharani

et al. 2024

Data in Brief

View full text Add to dashboard Cite

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

A novel dataset of potato leaf disease in uncontrolled environment

Shabrina,

Indarti,

Maharani

et al. 2024

Data in Brief

View full text Add to dashboard Cite

“…The suggested method obtains an F1-score of 96.33% and an overall accuracy of 98.66%. Using datasets from Apple (4 classes) and Tomato (10 classes), a thorough validation analysis is carried out, yielding remarkable accuracy of 96.42% and 94.25%, respectively [12]. The color attributes of the foliage image were utilized to localize the region of interest by the mixture model for region growth.…”

Section: Introductionmentioning

confidence: 99%

Classification of tea leaf disease using convolutional neural network approach

Hairah,

Septiarini,

Puspitasari

et al. 2024

IJECE

View full text Add to dashboard Cite

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.

show abstract

“…From the results using MobilenetV2 without data augmentation, an accuracy rate of 97.6% was achieved, and by performing data augmentation, an accuracy of 99.6% was achieved. A study by (Arshad et al, 2023) on various objects such as tomatoes, apples, and potato leaves from PlantVillage (Kaggle) yielded an accuracy rate of 94.25% using the PDDPNet method. Research by (Nishad et al, 2022) on potato leaf objects used the CNN algorithm with the VGG16 architecture and a dataset obtained from PlantVillage (Kaggle) and Mendeley totaling 2,580 images, split in an 80:20 ratio for training and testing data.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture

Brawijaya,

Rahmawati,

Haryanto

2024

pilar

View full text Add to dashboard Cite

Diseases affecting potato leaves frequently lead to significant setbacks for farmers, reducing the overall harvest and the quality of the potatoes. Given the critical need for prompt disease detection, this research introduces the use of the MobileNet framework grounded in the Convolutional Neural Network (CNN) for adept detection of potato leaf ailments. During the research, potato leaf images undergo processing, and their distinct features are gleaned using CNN. Then, harnessing the MobileNet framework, these images undergo classification to ascertain the existence of diseases. The aspiration is that the formulated model can pinpoint diseases with notable precision, rapid feedback, and enhanced computational adeptness. Initial findings underscore the potential of this methodology in discerning potato leaf diseases, providing renewed optimism for farmers grappling with plant health issues. Experiments using the Transfer Learning approach showed good performance in classification and displayed a high accuracy rate of 99.2%.

show abstract

PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction

Cited by 36 publications

References 44 publications

A novel dataset of potato leaf disease in uncontrolled environment

A novel dataset of potato leaf disease in uncontrolled environment

Classification of tea leaf disease using convolutional neural network approach

Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture

Contact Info

Product

Resources

About