Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier

Singh, Jaskaran; Kaur, Harpreet

doi:10.1007/978-3-030-00665-5_154

Cited by 24 publications

(17 citation statements)

References 12 publications

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“…Additional features such as color, texture, and shape can be extracted using the Gray Level Co-occurrence Matrix (GLCM) [7]. Based on the features extracted, the K-means algorithm can be used to segment the regions, and the K-nearest neighbour (KNN) classifier can be used for prediction from the extracted features [8][9][10]. Later, artificial intelligence was incorporated, and an optimized MSF-AdaBoost model was developed for classifying and monitoring powdery mildew on winter wheat.…”

Section: Figure 1 Input Dataset Categoriesmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF

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Section: Figure 1 Input Dataset Categoriesmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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“…Haar-like features and AdaBoost classifier is used to detect the affected portion of the leaf [6]. This proposed system [7], preprocess the input image and GLCM is used for texture feature extraction. The region-based segmentation is done using k-means clustering.…”

Section: Related Workmentioning

confidence: 99%

“…The region-based segmentation is done using k-means clustering. The disease is prediction using the KNN classifier [7]. This paper [8] proposes the advanced Neural Network (NN) to process hyperspectral data.…”

Section: Related Workmentioning

confidence: 99%

Detection and Classification of Paddy Crop Disease using Deep Learning Techniques

Kiruthika*¹,

S²,

R*³

et al. 2019

IJRTE

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Agricultural production plays a vital role in Indian economy. The biggest menace for a farmer is the various diseases that infect the crop. Quality and high production of crops is involved with factors like efficient detection of diseases in the crop. The disease detection though Naked-eye observation of expert can be prohibitively expensive and requires meticulous and scrupulous analysis to detect the disease. The existing systems on disease detection is not efficient enough in terms on real time basis. This paper presents an effective method for identification of paddy leaf disease. The proposed approaches involves pre-processing of input image and the paddy plant disease type is recognized using Gray-Level Co-occurrence Matrix (GLCM) technique and classifiers namely Artificial Neural Networks is used for better accuracy of detection. This method will be very useful to farmers to detect paddy diseases beforehand and thus prevent over usage of pesticides which in turn affects the crop production

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“…These algorithms are implemented based on the concept of pre-processing work of enhancement of image, image segmentation, image features are extracted and implementing various classifiers [5]. Most widely used classifiers are support vector machine (SVM) [6], decision tree, K-nearest algorithm (KNN) [7], random forest techniques (RF) [8], naive Baye technique (NB), logistic regression strategy (LR), rule generation [9]. Similarly, deep-learning-based algorithms, CNN, RCNN are produces the promising techniques in the detection of diseases of plant diseases.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Convolutional Neural Network for Plant Diseases Prediction

Poornima¹,

Sripriya²,

Alrasheedi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products. The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agriculture. Manual system to monitor the diseases in plant is time consuming and report a lot of errors. There is high demand for technology to detect the plant diseases automatically. Recently image processing approach and deep learning approach are highly invited in detection of plant diseases. The diseases like late blight, bacterial spots, spots on Septoria leaf and yellow leaf curved are widely found in plants. These are the main reasons to affects the plants life and yield. To identify the diseases earliest, our research presents the hybrid method by combining the region based convolutional neural network (RCNN) and region based fully convolutional networks (RFCN) for classifying the diseases. First the leaf images of plants are collected and preprocessed to remove noisy data in image. Further data normalization, augmentation and removal of background noises are done. The images are divided as testing and training, training images are fed as input to deep learning architecture. First, we identify the region of interest (RoI) by using selective search. In every region, feature of convolutional neural network (CNN) is extracted independently for further classification. The plants such as tomato, potato and bell pepper are taken for this experiment. The plant input image is analyzed and classify as healthy plant or unhealthy plant. If the image is detected as unhealthy, then type of diseases the plant is affected will be displayed. Our proposed technique achieves 98.5% of accuracy in predicting the plant diseases.

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Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier

Cited by 24 publications

References 12 publications

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Detection and Classification of Paddy Crop Disease using Deep Learning Techniques

Hybrid Convolutional Neural Network for Plant Diseases Prediction

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