Detection and Recognition of Diseases from Paddy Plant Leaf Images

Mohan, Krishna; Balasubramanian, M.; Palanivel, S.

doi:10.5120/ijca2016910505

Cited by 47 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23][24] introduced classifiers that generally use in agriculture are SVM, k-NN, backpropagation neural network, and decision tree, which helps to narrow the option of choosing a suitable algorithm. Comparing k-NN and SVM use in detecting rice diseases, in [25] concluded that k-NN has better accuracy. Thus, k-NN is applied in this research because of its simplicity, effectiveness, nonparametric, and its wide range of usage in image and spatial classification [26].…”

Section: Building the Classifiermentioning

confidence: 99%

Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data

Muliady¹,

Sze²,

Koo³

et al. 2021

IJEECS

View full text Add to dashboard Cite

<span>Crop management including the efficient use of nitrogen (N) fertilizer is important to ensure crop productivity. Human error in judging the leaf greenness when using the leaf color chart (LCC) to estimate the rice plant N nutrient status has encouraged numerous researchers to implement a machine-learning algorithm but experienced some issues in calibration and lighting. The datasets are created at 6.00-7.00AM (consistent lighting) and including light intensity, so each dataset contains RGB value and light intensity as inputs, and LCC value as a target. A system consists of a smartphone with an application that prevents user from taking an image if the light intensity is not in 2000-3500 lux, and a computer for preprocessing and classification purposes were developed. The preprocessing included cropping, splitting the rice leaf images, and calculating the average RGB values. A k-NN classifier is implemented and by using a cross-validation method is found k=5 gives the best accuracy of 97,22%. The in-site test of the system also works with an accuracy of 96.40%. </span>

show abstract

Section: Building the Classifiermentioning

confidence: 99%

Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data

Muliady¹,

Sze²,

Koo³

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…There are various plants which gets effected and are chosen by the researchers for their experiments, some of them over the years are summarised in Table 3. There are many diseases which occur due to bacteria in the plants, such as bacterial brown spot [17, 32–37], bacterial soft rot [17], citrus canker [38], rice spot [31], ashen mould [39], myrothecium [37], alternaria [37], scab angular [40], downy mildew [40, 41], leaf curl [42], bacterial blight [43]. Bai et al [44], Zhang et al [40, 41] worked on cucumber with 129, 300 and 420 images in their dataset.…”

Section: Categorical Classification Of Algorithmic Techniquesmentioning

confidence: 99%

Review of image processing approaches for detecting plant diseases

Sinha

Shekhawat

2020

IET Image Processing

View full text Add to dashboard Cite

“…Jagan and Mohan focused on paddy diseases using Scale Invariant Feature Transform (SIFT) on leaf images. They experimented on k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) on SIFT features [3]. Phadikar analyzed rice leaf diseases using morphological features, radial distribution of pathology on leaves and histogram equalization features.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

Altan

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

Deep Learning (DL) is a high capable machine learning algorithm which composed the advanced image processing as feature learning and supervised learning with detailed models with many hidden layers and neurons. DL demonstrated its efficiency and robustness in many big data problems, computer vision, and more. Whereas it has an increasing popularity day by day, it has still some deficiencies to construe the relationship between learned feature maps and spatial information. Capsule network (CapsNET) is proposed to overcome the shortcoming by excluding the pooling layer from the architecture and transferring spatial information between layers by capsule. In this paper, CapsNET architecture was proposed to evaluate the performance of the model on classification of plant leaf diseases using simple reduced capsules on leaf images. Plant leaf diseases are common and prevalent diseases that disrupt harvesting and yielding for agriculture. CapsNET has capability of detailed analysis for even small stains that may lead seed dressing time and duration. The proposed CapsNET model aimed at assessing the applicability of various feature learning models and enhancing the learning capacity of the DL models for bell pepper plants. The healthy and diseased leaf images were fed into the CapsNET. The proposed CapsNET model reached high classification performance rates of 95.76%, 96.37%, and 97.49% for accuracy, sensitivity, and specificity, respectively.

show abstract

Detection and Recognition of Diseases from Paddy Plant Leaf Images

Cited by 47 publications

References 12 publications

Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data

Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data

Review of image processing approaches for detecting plant diseases

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

Contact Info

Product

Resources

About