Plant Leaf Disease Detection Using Adaptive Neuro-Fuzzy Classification

Sabrol, Hiteshwari; Kumar, Satish

doi:10.1007/978-3-030-17795-9_32

Cited by 23 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it was not effective in detecting various diseases on the same plant. Sabrol and Kumar 25 developed a gray‐level spatial dependence model in 2019 for extracting the features and classifying the plant disease. It used an ANFIS classifier to generate more rules and input variables.…”

Section: Motivationmentioning

confidence: 99%

Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network

Shanmugam¹,

Rao

Rao³

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

Summary Rice is the major crop in India. Early prevention and timely identification of plant leaf diseases are important for increasing production. Hence, an effective sunflower earthworm algorithm and student psychology based optimization (SEWA‐SPBO) based deep maxout network is developed to classify different types of diseases in rice plant leaf. The SEWA is the combination of sunflower optimization (SFO) and earthworm algorithm (EWA). Initially, the network nodes simulated in the environment capture the plant leaf images and are routed to the sink node for disease classification. After receiving the plant images at the sink node, the image is preprocessed using a Gaussian filter. Next to preprocessing, segmentation using the black hole entropic fuzzy clustering (BHEFC) mechanism is performed. Then, data augmentation is applied to segmented image results and disease classification is done by a deep maxout network. The training of the deep maxout network is done using the proposed SEWA‐SPBO algorithm. The proposed method detects the leaf disease more accurately with limited time and shows higher accuracy. Moreover, the proposed method attains higher performance with metrics, like accuracy, sensitivity, and specificity as 93.626%, 94.626%, and 90.431%, respectively.

show abstract

Section: Motivationmentioning

confidence: 99%

Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network

Shanmugam¹,

Rao

Rao³

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…They achieved maximum estimation rates of 92.55% and 92.3% in classifier training and testing with eight clusters. In 2020, Sabrol and Kumar 32 extracted basic statistical features of the images on a gray‐scale and sent them to the ANFIS classifier to classify different types of tomato and brinjal eggplant diseases. In their method, the ANFIS classification accuracy of pattern‐based features reached 98.0% and 90.7% for the respective BPDS 1.0 and TPDS 1.0 datasets 32 …”

Section: Related Workmentioning

confidence: 99%

“…In their method, the ANFIS classification accuracy of pattern-based features reached 98.0% and 90.7% for the respective BPDS 1.0 and TPDS 1.0 datasets. 32 All of the above research studies classify plant diseases or pests on different datasets, most of which include only one type of disease or pest. This is why our database was prepared manually and differently.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques

2022

View full text Add to dashboard Cite

BACKGROUND: Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore, the damage to the tomato tree must be controlled in its early stages. It is difficult for farmers to distinguish Alternaria disease from leafminer pest at the early and middle stages of their outbreak on tomato leaves. In the present study, 272 tomato leaf images were prepared from the farm of the Vali-e-Asr University of Rafsanjan, including 100 healthy leaves and 172 infected leaves with both Alternaria and leafminer at the initial stages. The image processing technique, texture, neural networks and adaptive network-based fuzzy inference system (ANFIS) classifiers were used to diagnose Alternaria disease and leafminer pest on this dataset. RESULTS:The results showed that the ANFIS classifier achieved an accuracy of 84.71% when performing an equal error rate, 87.78% in the area under the curve, and 85.23% in 3.26 s on the central processing unit for the segmentation of Alternaria disease and leafminer pest in RGB color space. Also, the accuracy of 90% and 98% were obtained for segmentation and classification on the PlantVillage dataset in YCBCR color space.CONCLUSION: The present study suggests a high classification accuracy for an intelligent selection of pixel values to train the ANFIS classifier. This classifier has high accuracy and speed, low sensitivity to the light intensity of images, and practical application in diagnosing various diseases and pests on numerous datasets.

show abstract

“…About 70% of the sample data is used for training and 30% is used for testing. Sugeno FIS model always computes predictions in the form of numeric data [39].…”

Section: Anfis For Covid-19mentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

View full text Add to dashboard Cite

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

show abstract

Plant Leaf Disease Detection Using Adaptive Neuro-Fuzzy Classification

Cited by 23 publications

References 12 publications

Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network

Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network

Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Contact Info

Product

Resources

About