A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases

Gobalakrishnan, N; Pradeep, K.; Raman, Chandrasekharan; Ali, L. Javid; Gopinath, M. P.

doi:10.1109/iccsp48568.2020.9182046

Cited by 47 publications

(16 citation statements)

References 19 publications

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“…This methodology of doing systematic literature review is present in this section. The researchers in [69] have done a focus SLR in the field of image capturing, image processing and machine learning approaches in the field of agriculture (plant diseases). In this section research questions and the factors of motivation are mentioned which is a part of systematic literature review.…”

Section: Methodsmentioning

confidence: 99%

A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture

et al. 2021

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Automation in agriculture nowadays is the main focus and area of development for various countries. The population rate of the world is increasing rapidly and will be double in upcoming decades and the need of food is also increasing accordingly. To meet this rapid growth in demand, agriculture automation is the best solution. Traditional strategies employed by farmers are not efficient enough to fulfill the rising demand. Improper use of nutrients, water, fertilizers and pesticides disturbs the agricultural growth and the land remains barren with no fertility. This research paper presents different control strategies used to automate agriculture such as: IoT, aerial imagery, multispectral, hyperspectral, NIR, thermal camera, RGB camera, machine learning, and artificial intelligence techniques. Problems in agriculture like plant diseases, pesticide control, weed management, irrigation and water management can easily be solved by different automated and control techniques mentioned above. Automation by advance control strategies of agricultural methods have verified to increase the crops yield and also the soil fertility become strong. This research paper reviews and observe the work of different researchers to present a brief summary about the trends in smart agriculture and also provides the work flow and revenue of smart agriculture system in figure 15 using technologies verified by researchers in their research papers.

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Section: Methodsmentioning

confidence: 99%

A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture

et al. 2021

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“…monochromatic leaves ( Gehan et al, 2017 ) and berries ( Underhill et al, 2020 )], un-patterned in their expression [e.g. lesions ( Arnal Barbedo, 2013 ; Gobalakrishnan et al, 2020 ; Xie et al, 2020 )], or have highly predictable patterns (e.g. nectar guides).…”

Section: Introductionmentioning

confidence: 99%

“…nectar guides). These color phenotypes are readily extractable using existing image processing approaches that are not suited for the complex suite of color patterns represented in our coleus population ( Arnal Barbedo, 2013 ; Gobalakrishnan et al, 2020 ; Xie et al, 2020 ). Here, we address the need for enhanced tools to extract and analyze complex color patterns.…”

Section: Introductionmentioning

confidence: 99%

Quantitative dissection of color patterning in the foliar ornamental coleus

Coneva

Robbins

et al. 2021

Plant Physiology

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Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called “ColourQuant,” we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we are able to analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.

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“…Today, with the development of technology, it is seen that auxiliary tools have been created for farmers. Recently, pre-network-based methods and image processing algorithms are frequently used in the classification and automatic detection of leaf diseases of various plants (Gargade & Khandekar, 2019;Gobalakrishnan et al, 2020;Shrivastava, 2021;Tahir et al, 2021). When the studies are looked back, it is seen that mostly classical machine learning algorithm methods are used (Annabel et al, 2019;Gobalakrishnan et al, 2020;Shrivastava, 2021).…”

Section: Introductionmentioning

confidence: 99%

Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

ÇETİNER

2021

Mühendislik Bilimleri Ve Tasarım Dergisi

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It is difficult to constantly control apple trees in farmland. In case of a disease on tree leaves, the risk of disease transmission to other leaves is high. It is necessary to prevent further deterioration of the plant by performing automatic detection of the disease in the early period. If the disease detection is delayed, the planned production cannot be realized. It is too late if diseases are detected by a farmer or agronomist. In addition, as the agricultural lands grow, the number of experts needed increases accordingly. For these reasons, leaf images of apple trees are grouped into 4 different classes: apple peel, leaf rust, healthy apple and multiple disease states. In the proposed method, noise removal in the images, detection of the relevant area and histogram equalization on the YUV color space are performed. Due to the unbalanced class distribution in the data set used, data augmentation was applied for the minority classes with the SMOTE method. Afterwards, features are extracted using pre-trained network models DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50V2. Extracted features were classified with a CNN-based method developed with an accuracy of 99%.

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A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases

Cited by 47 publications

References 19 publications

A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture

A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture

Quantitative dissection of color patterning in the foliar ornamental coleus

Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

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