Machine Learning Based Plant Leaf Disease Detection and Severity Assessment Techniques: State-of-the-Art

Pukkela, Pragati; Borra, Surekha

doi:10.1007/978-3-319-65981-7_8

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…Their experiments showed fine-grained DSA task is substantially more difficult than simple classification of different diseases since in this case there exists a high intra-class similarity and low interclass variance ( Xie et al., 2015 ). Moreover, Pukkela and Borra (2018) summarized the findings of various machine-learning-based plant DSA systems. They also outlined popular metrics used in classical DSA tasks to measure the performance of an algorithm, e.g., ratio of infected area (RIA), lesion color index (LCI), damage severity index (DSI), and infection per region (IPR).…”

Section: Related Workmentioning

confidence: 99%

DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis

Ilyas

Jin²,

Siddique³

et al. 2022

Front. Plant Sci.

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The emergence of deep neural networks has allowed the development of fully automated and efficient diagnostic systems for plant disease and pest phenotyping. Although previous approaches have proven to be promising, they are limited, especially in real-life scenarios, to properly diagnose and characterize the problem. In this work, we propose a framework which besides recognizing and localizing various plant abnormalities also informs the user about the severity of the diseases infecting the plant. By taking a single image as input, our algorithm is able to generate detailed descriptive phrases (user-defined) that display the location, severity stage, and visual attributes of all the abnormalities that are present in the image. Our framework is composed of three main components. One of them is a detector that accurately and efficiently recognizes and localizes the abnormalities in plants by extracting region-based anomaly features using a deep neural network-based feature extractor. The second one is an encoder–decoder network that performs pixel-level analysis to generate abnormality-specific severity levels. Lastly is an integration unit which aggregates the information of these units and assigns unique IDs to all the detected anomaly instances, thus generating descriptive sentences describing the location, severity, and class of anomalies infecting plants. We discuss two possible ways of utilizing the abovementioned units in a single framework. We evaluate and analyze the efficacy of both approaches on newly constructed diverse paprika disease and pest recognition datasets, comprising six anomaly categories along with 11 different severity levels. Our algorithm achieves mean average precision of 91.7% for the abnormality detection task and a mean panoptic quality score of 70.78% for severity level prediction. Our algorithm provides a practical and cost-efficient solution to farmers that facilitates proper handling of crops.

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Section: Related Workmentioning

confidence: 99%

DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis

Ilyas

Jin²,

Siddique³

et al. 2022

Front. Plant Sci.

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“…It facilitated the integration of big data's many forms, as well as its processing, and it created intricate nonlinear linkages between training data and independent predictors (covariates) [16]. Machine learning allows for a fully automated and subjective determination of feature importance, as opposed to the manual and subjective computation of weights of specific abiotic criteria in the suitability result [69].…”

Section: Recent Developments In Machine-learning-based Cropland Suita...mentioning

confidence: 99%

GIS-Based Cropland Suitability Prediction Using Machine Learning: A Novel Approach to Sustainable Agricultural Production

Radočaj

Jurišić

2022

Agronomy

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The increasing global demand for food has forced farmers to produce higher crop yields in order to keep up with population growth, while maintaining sustainable production for the environment. As knowledge about natural cropland suitability is mandatory to achieve this, the aim of this paper is to provide a review of methods for suitability prediction according to abiotic environmental criteria. The conventional method for calculating cropland suitability in previous studies was a geographic information system (GIS)-based multicriteria analysis, dominantly in combination with the analytic hierarchy process (AHP). Although this is a flexible and widely accepted method, it has significant fundamental drawbacks, such as a lack of accuracy assessment, high subjectivity, computational inefficiency, and an unsystematic approach to selecting environmental criteria. To improve these drawbacks, methods for determining cropland suitability based on machine learning have been developed in recent studies. These novel methods contribute to an important paradigm shift when determining cropland suitability, being objective, automated, computationally efficient, and viable for widespread global use due to the availability of open data sources on a global scale. Nevertheless, both approaches produce invaluable complimentary benefits to cropland management planning, with novel methods being more appropriate for major crops and conventional methods more appropriate for less frequent crops.

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“…Image processing is a procedure to change an input image onto digital frame and performed a couple of process, with the true objective to get a redesigned picture or to separate some significant data from it. It is a sort of signal dispensation which input is image, like video edge or photo and result may be picture or characteristics related with that image [21,22]. The image preprocessing include filtering, color conversion and detail enhancement of image [23,31].As per the recent researches that has been conducted over the related image pre-processing techniques carried out for diagnosis of paddy diseases are mentioned in Table 2.…”

Section: Image Pre-processingmentioning

confidence: 99%

Review on phenotype traits analysis of Rice plants by using Machine Learning Techniques

Patel

Sharaff

2022

Preprint

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Introduction: Rice crops are one of the main source of food for human being. According to data, population of the world increasing over the past decades. Due to continuous increase in population,need of rice is also increases. Rice plant diseases is one of the important factor in the production of rice. As the rice plant disease increase in the paddy, the production of rice decreases.Rice plant diseases are considered as a raising factor behind the agricultural, economic and communal loss in the upcoming development of the agricultural field. Problem :In order to increase the production of rice, we have to keep each rice plant in the paddy healthy. In order to do diagnosis of rice plant diseases, there are various traditional methods but this method need experts and continuous observation. Hence the traditional methods are very costly and time consuming. Finding: To solve the above problem(Problem of diagnosis of rice plant disease) many researcher used the image processing,machine learning,deep learning,Internet of thing techniques. In this paper, we have considered the brief review on the image processing,machine learning,deep learning,Internet of thing techniques which are useful for the diagnosis of rice plant disease.

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Machine Learning Based Plant Leaf Disease Detection and Severity Assessment Techniques: State-of-the-Art

Cited by 17 publications

References 39 publications

DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis

DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis

GIS-Based Cropland Suitability Prediction Using Machine Learning: A Novel Approach to Sustainable Agricultural Production

Review on phenotype traits analysis of Rice plants by using Machine Learning Techniques

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