Journal ArticleDOI
Study of digital image processing techniques for leaf disease detection and classification
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TLDR
A comprehensive discussion on the diseases detection and classification performance is presented based on analysis of previously proposed state of art techniques particularly from 1997 to 2016.Abstract:
In this paper, we address a comprehensive study on disease recognition and classification of plant leafs using image processing methods. The traditional manual visual quality inspection cannot be defined systematically as this method is unpredictable and inconsistent. Moreover, it involves a remarkable amount of expertise in the field of plant disease diagnostics (phytopathology) in addition to the disproportionate processing times. Hence, image processing has been applied for the recognition of plant diseases. The paper has been divided into two main categories viz. detection and classification of leafs. A comprehensive discussion on the diseases detection and classification performance is presented based on analysis of previously proposed state of art techniques particularly from 1997 to 2016. Finally, discussed and classify the challenges and some prospects for future improvements in this space.read more
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Journal ArticleDOI
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Dhivya Elavarasan,P. M. Durai Raj Vincent,Vishal Sharma,Albert Y. Zomaya,Kathiravan Srinivasan +4 more
TL;DR: This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature and compares one approach with other using various error measures like Root Mean Square Error (RMSE) and Coefficient of Determination (R2).
Journal ArticleDOI
Plant disease detection using computational intelligence and image processing
TL;DR: Common infections along with the research landscape at different stages of such detection systems are discussed and the modern feature extraction techniques are analyzed for identifying those that appear to work well covering several crop categories.
Journal ArticleDOI
A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades
TL;DR: It is proposed that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.
Journal ArticleDOI
A novel computer vision based neutrosophic approach for leaf disease identification and classification
TL;DR: A novel fuzzy set extended form neutrosophic logic based segmentation technique is used to evaluate the region of interest and a new feature set is promising and 98.4% classification accuracy is achieved.
Proceedings ArticleDOI
Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques
TL;DR: A system that is used to classify and detect plant leaf diseases using deep learning techniques and has excellent accuracy in training and testing is presented.
References
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Journal ArticleDOI
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Journal ArticleDOI
Fast and Accurate Detection and Classification of Plant Diseases
TL;DR: The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases and can achieve 20% speedup over the approach proposed in [1].
Journal ArticleDOI
An image-processing based algorithm to automatically identify plant disease visual symptoms.
Anyela Camargo,Jeremy S. Smith +1 more
TL;DR: An image-processing based method that identifies the visual symptoms of plant diseases, from an analysis of coloured images, showed that the developed algorithm was able to identify a diseased region even when that region was represented by a wide range of intensities.
Journal ArticleDOI
Digital image processing techniques for detecting, quantifying and classifying plant diseases.
TL;DR: A survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum, providing a comprehensive and accessible overview of this important field of research.
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