Sugarcane leaf disease detection and severity estimation based on segmented spots image

Ratnasari, Evy Kamilah; Mentari, Mustika; Dewi, Ratih Kartika; Ginardi, Raden Venantius Hari

doi:10.1109/icts.2014.7010564

Cited by 45 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The system’s effectiveness was then assessed using performance indicators like F-measure, accuracy, sensitivity, recall, and specificity value. In ( Ratnasari et al., 2014 ), the authors introduced a sugarcane leaf disease identification technique using RGB pictures. Only three categories of diseases—ring spot, rust spot, and yellow spot—have undergone verification using the suggested system.…”

Section: Related Workmentioning

confidence: 99%

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

Ullah,

Khan,

Almakdi

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

show abstract

Section: Related Workmentioning

confidence: 99%

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

Ullah,

Khan,

Almakdi

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…It was argued that early detection and estimation could be the way to reduce severity and save the crop yield to better extent. The combination of gray level co-occurrence matrix (GLCM) as a feature and support vector machines as classifier for spot disease shows the accuracy and error severity estimation about 80% and 5.73,respectively [3]. Fuzzy C-means clustering and rough set were employed to detect the insect pest of sugarcane cotton aphis.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Sugarcane Foliar Diseases Methods and Datasets

Daphal,

Koli

2020

IJEAT

View full text Add to dashboard Cite

Agriculture is the major part of the Indian economy as it provides key support to social and economic development of the country. Sugarcane is the leading cash crop in various states of India which has larger share in the net agriculture produce. Recently, researches have highlighted the impact of different disease on various plants. Estimated loss is much severe for the sugarcane crop via foliar diseases. Foliar diseases like rust, eye spot, mosaic and banded chlorosis may hamper the overall productivity of sugarcane and sugar recovery rate (RR). Early prediction of these diseases may limit the losses in terms of produce net benefits. This paper addresses the concerns, types of the foliar disease and researches undertaken to overcome the problems related to the diseases. Morphological characters of these diseases may help in identifying the representative features and to use them for the optimum classification. Currently the use of deep neural networks (DNN) is encouraged for the classification. DNN demands the huge and accurate databases. Intuitively the use and important methods used in database creation for disease diagnostic system (DDS) has been highlighted in the paper. Modifications made to the Convolutional Neural Network architecture have suggested the improved performance in terms of recognition accuracy (RA) and lesser recognition time.

show abstract

“…We proposed a new system to detect and estimate the soybean leaf disease blight and leaf spots using threshold-based segmented spots image and classify the colors in (R, G, B) color space using incremental k-means clustering technique. The Otsu method is used for spot disease lesion segmentation from (R, G, B) chnnel of color space [17]. The threshold value of the pixels was computed according to the masked pixels.…”

Section: Leaf Disease Segmentationmentioning

confidence: 99%

Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier

Jadhav

Udup²,

Patil³

2019

IJECE

View full text Add to dashboard Cite

Digital audio is one of the most important types of data at present. It is used in several applications, such as human knowledge and many security and banking applications. A digital voice signal is usually of a large size where the acoustic signal consists of a set of values distributed in one column (one channel) (mono signal) or distributed in two columns (two channels) (stereo signal), these values usually are the results of sampling and quantization of the original analogue voice signal. In this paper we will introduce a method which can be used to create a signature or key, which can be used later to identify or recognize the wave file. The proposed method will be implemented and tested to show the accuracy and flexibility of this method<p> </p>

show abstract

Sugarcane leaf disease detection and severity estimation based on segmented spots image

Cited by 45 publications

References 6 publications

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

Identification of Sugarcane Foliar Diseases Methods and Datasets

Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier

Contact Info

Product

Resources

About