FCNN-LDA: A Faster Convolution Neural Network model for Leaf Disease identification on Apple's leaf dataset

Agarwal, Mohit; Kaliyar, Rohit Kumar; Singal, Gaurav; Gupta, Suneet Kr.

doi:10.1109/icts.2019.8850964

Cited by 33 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study firstly extends the grape leaf disease image via digital image processing technique, creating the grape leaf disease dataset (GLDD). In Agarwal et al [13], a CNN method was established for identifying the disease in apple and it has 2 densely connected, 3 max pooling layers, and 3 convolution layers. This module was made afterward analysis by differing amount of convolution layers from two to six and initiate that three layers were provided an optimal accuracy.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Al‐Wesabi¹,

Albraikan²,

Hilal³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases. With this motivation, this paper introduces a novel AI enabled apple leaf disease classification (AIE-ALDC) technique for precision agriculture. The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes. In addition, the AIE-ALDC technique includes a Capsule Network (CapsNet) based feature extractor to generate a helpful set of feature vectors. Moreover, water wave optimization (WWO) technique is employed as a hyperparameter optimizer of the CapsNet model. Finally, bidirectional long short term memory (BiLSTM) model is used as a classifier to determine the appropriate class labels of the apple leaf images. The design of AIE-ALDC technique incorporating the WWO based CapsNet model with BiLSTM classifier shows the novelty of the work. A wide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique. The experimental results demonstrate the promising performance of the AIE-ALDC technique over the recent state of art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

“…A method of separating waves are chosen arbitrarily amongst [1, k max ], k max denotes a predetermined parameter which is higher compared to one, and generates an orphan wave in all dimensions based on Eq. (13).…”

Section: Breaking Statementioning

confidence: 99%

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Al‐Wesabi¹,

Albraikan²,

Hilal³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Leaf diseases of apples refer to the common diseases found on apple leaves, namely scab, rust, and powdery mildew, among others [1]. Apple scab, caused by a fungal pathogen, is one of the most economically important fungal diseases of apple in the world [2,3]. The symptoms of apple scab are clearly visible fungal structures on the surface of the leaf.…”

Section: Introductionmentioning

confidence: 99%

“…Recent advances in computer vision enabled by deep leaning have paved the way for more accurate disease diagnosis. Using large public datasets of diseased and healthy plants and leaves, a CNN can be trained to identify various leaf diseases [8,2]. With the increasing availability of smartphones, the approach of training deep-learning models on a large scale has emerged as a clear way to diagnose crops on a large scale [5].…”

Section: Introductionmentioning

confidence: 99%

“…It is important to identify the cause at the root, which is beneficial and time-saving for both the agricultural sector as well as the farmers. So far, to our knowledge, there are some works on leaf disease detection in apples, and these datasets have fewer classes [2,5]. Only one work has looked at the 2020 plant pathology dataset, but it has the limitation of lacking augmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AFD-Net: Apple Foliar Disease Multi Classification using Deep Learning on Plant Pathology Dataset

Yadav¹,

Thakur²,

Saxena³

et al. 2022

Preprint

View full text Add to dashboard Cite

Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way to detect and diagnose these plant diseases on the leaf. In this work, the model AFD-Net was developed to detect and identify various leaf diseases in apple trees. The dataset is from Kaggle 2020 and 2021 and was financially supported by the Cornell Initiative for Digital Agriculture. A AFD-Net was proposed for leaf disease classification in apple trees and the results of the efficiency of the model are compared with other state-of-the-art deep learning approaches. The results of the experiments in the validation dataset show that the proposed AFD-Net model achieves the highest values compared to other deep learning models in the original and extended datasets with 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021.

show abstract

A Partcle Swarm Optimization Based Approach for Filter Pruning in Convolution Neural Network for Tomato Leaf Disease Classification

Agarwal

Gupta

Garg

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

FCNN-LDA: A Faster Convolution Neural Network model for Leaf Disease identification on Apple's leaf dataset

Cited by 33 publications

References 13 publications

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

AFD-Net: Apple Foliar Disease Multi Classification using Deep Learning on Plant Pathology Dataset

A Partcle Swarm Optimization Based Approach for Filter Pruning in Convolution Neural Network for Tomato Leaf Disease Classification

Contact Info

Product

Resources

About