Automatic classification of monocots and dicots using SVM classifier

Nasreen, P Najiya; Kumar, Arun; Nabeel, P Asjad

doi:10.1109/infosci.2016.7845295

Cited by 4 publications

(1 citation statement)

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“…The images from the analysed dataset are first loaded, and image analysis is then carried out on them. There are many sectors that rely on the classification and detection of leaf diseases [18]. Image processing techniques are critical for the early diagnosis of plant leaf disease.…”

Section: Proposed Model Figure Proposed Model Frameworkmentioning

confidence: 99%

Corn Leaf Disease Detection with Pertinent Feature Selection Model Using Machine Learning Technique with Efficient Spot Tagging Model

Noola¹,

Basavaraju²

2021

RIA

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Crop diseases constitute a substantial threat to food safety but, due to the lack of a critical basis, their rapid identification in many parts of the world is challenging. The development of accurate techniques in the field of image categorization based on leaves produced excellent results. Plant phenotyping for plant growth monitoring is an important aspect of plant characterization. Early detection of leaf diseases is crucial for efficient crop output in agriculture. Pests and diseases cause crop harm or destruction of a section of the plant, leading to lower food productivity. In addition, in a number of less-developed countries, awareness of pesticide management and control, as well as diseases, is limited. Some of the main reasons for decreasing food production are toxic diseases, poor disease control and extreme climate changes. The quality of farm crops may be influenced by bacterial spot, late blight, septoria and curved yellow leaf diseases. Because of automatic leaf disease classification systems, action is easy after leaf disease signs are detected. Applying image processing and machine learning methodologies, this research offers an efficient Spot Tagging Leaf Disease Detection with Pertinent Feature Selection Model using Machine Learning Technique (SPLDPFS-MLT). Different diseases deplete chlorophyll in leaves generating dark patches on the surface of the leaf. Machine learning algorithms can be used to identify image pre-processing, image segmentation, feature extraction and classification. Compared with traditional models, the proposed model shows that the model performance is better than those existing.

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Section: Proposed Model Figure Proposed Model Frameworkmentioning

confidence: 99%