Detection of Plant Disease on Leaves using Blobs Detection and Statistical Analysis

Taujuddin, Nik Shahidah Afifi; Mazlan, A.I.A; Ibrahim, Rosziati; Sari, Suhaila; Ghani, Azfar; Senan, Norhalina; Muda, W.H.N Wan

doi:10.14569/ijacsa.2020.0110852

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…Lab channel result Each color space is analyzed for statistical characteristics. Statistical characteristics are calculated in the form of Mean, standard deviation, and skewness for each channel [43]. The Mean is the average value, the standard deviation is the statistical value used to determine the closeness of the statistical sample to the average of the data, and skewness is the level of asymmetry or the distance of symmetry from a distribution.…”

Section: Figure 12mentioning

confidence: 99%

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

Advances in Biological Sciences Research

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Chili is a horticultural crop that has high economic value in Indonesia. The productivity level of chili in the country is not proportional to the level of consumption, one of the causes is malnutrition. Each plant requires different amounts of macronutrients and micronutrients to support plant growth and development. Chili plants that lack or excess macronutrients show different visual symptoms. Digital Image Processing is a non-destructive method that is useful for determining plant health conditions based on visual symptoms of chili leaves. The combination of digital image processing and learning methods such as the Support Vector Machine (SVM) helps classify the types of macronutrient deficiencies in order to obtain a nutrient solution. In this study, there are several stages in determining macronutrient deficiencies in chili plants, namely image acquisition, pre-processing, feature extraction, to classification using SVM with several kernels. Based on the experimental results in this study, the SVM method can help modern farmers to determine the health condition of plants non-destructively with 97.76% accuracy using a Polynomial kernel. Applying this system to an intelligent farming system is expected to support the realization of precision agriculture in Indonesia.

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Section: Figure 12mentioning

confidence: 99%

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

Advances in Biological Sciences Research

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show abstract

“…Today, detecting plant diseases is challenging and requires particular care. Fungal infections, bacteria, viruses, and nematodes produce spots on leaves or stems, brown or black lesions, death of lower leaves, yellowing of lower leaves, and black specks [11]. Each disease has its technique of prevention.…”

Section: Literature Reviewmentioning

confidence: 99%

Brown Spot Disease Severity Level Detection using Binary-RGB Image Masking

Taujuddin¹,

Halim²,

Norsuha³

et al. 2021

IJACSA

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Agriculture is known as one of the main factor for a growth of a country. Paddy plantation is the most widely planted crop in Malaysia. The rice produced is the main food source to Malaysian's people and source of income to this country as well. However, a disease known as Brown Spot (BS) attacks the paddy plants and threats their quality. This disease caused by bipolar fungus, which represent by the development of an oval, dark brown to purplish-brown spot on leaf. This disease observed as among the hazardous disease that may result in degradation of paddy production. Brown Spot disease could spread through airborne spores from plant to plant on the field. In this research, a system that could help people, especially farmers, to detect the disease at early stage is developed. The real image capture at paddy field is processed in the MATLAB software with image enhancement, background removal as well as binary and RGB image masking process. To determine the Brown Spot area, pixel intensity between the infected and noninfected areas is calculated. The severity level table developed by Horsfall and Heuberger is then used as reference to classify the severity level of Brown Spot disease. A GUI is created to detect the Brown Spot disease automatically. From the study conducted, the accuracy of Brown Spot detection is approximately 89% accurate compared to manual evaluation by plant pathology.

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A Review on Plant Diseases Classification Methods Using Machine Learning Algorithms

Anuar,

Sani,

Sani

et al. 2024

SpringerBriefs in Applied Sciences and Technology

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Detection of Plant Disease on Leaves using Blobs Detection and Statistical Analysis

Cited by 10 publications

References 7 publications

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

Brown Spot Disease Severity Level Detection using Binary-RGB Image Masking

A Review on Plant Diseases Classification Methods Using Machine Learning Algorithms

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