Identification of Maize Leaf Diseases Cause by Fungus with Digital Image Processing (Case Study: Bismarak Village Kupang District - East Nusa Tenggara)

Overbeek, Marlinda Vasty; Kaesmetan, Yampi R; Tobing, Fenina Adline Twince

doi:10.1109/conmedia46929.2019.8981843

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…This, in turn, it allows farmers to recognize the diseases at an early stage and provides valuable knowledge to monitor the crop condition. www.ijacsa.thesai.org Image acquisition, image pre-processing, image segmentation, feature extraction, and classification are the image processing stages required for Brown Spot disease diagnosis used in [15][16][17]. These processes are done on the captured image of infected plants.…”

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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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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“…The results show promise for real-time disease detection in the agricultural industry, contributing to the preservation of crop productivity. M. V. Overbeek et al [23] propose a system for accurate detection of fungal diseases in maize leaves. Utilizing digital image processing, the Sobel operator extracts shape features, and a multiclass Support Vector Machine with a Radial Basis Function kernel ensures effective detection.…”

Section: Introductionmentioning

confidence: 99%

Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm

Alwan Fauzi,

Iwan Syarif,

Tessy Badriyah

2023

EMITTER Int'l J. of Engin. Technol.

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Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.

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