Visible-thermal database of rice field

Zamani, Seyed Alireza; Baleghi, Yasser

doi:10.17632/9xg52j8tmw.1

Cited by 1 publication

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“…Datasets were obtained in this study from a paddy in Amol, Mazandaran Province, Iran, with geographical coordinates of length 52.453171, width 36.601498 and height 12-meters [24]. The requirements are as follows: The rice field visible-thermal database contains 100 pairs of photos with a size of 595 × 385 pixels (100 visible images and 100 thermal (near-infrared) images).…”

Section: Experimental Setup 41 Dataset Collection and Descriptionmentioning

confidence: 99%

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Prasomphan¹

2023

Computer Systems Science and Engineering

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Establishing a system for measuring plant health and bacterial infection is critical in agriculture. Previously, the farmers themselves, who observed them with their eyes and relied on their experience in analysis, which could have been incorrect. Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light, both visible and eye using a drone. The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles (UAVs) with an ensemble classification technique. Convolution neural networks in unmanned aerial vehicles image were used. To convey this interest, the rice's health and bacterial infection inside the photo were detected. The project entailed using pictures to identify bacterial illnesses in rice. The shape and distinct characteristics of each infection were observed. Rice symptoms were defined using machine learning and image processing techniques. Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria. The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

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Section: Experimental Setup 41 Dataset Collection and Descriptionmentioning

confidence: 99%

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Prasomphan¹

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

Visible-thermal database of rice field

Cited by 1 publication

References 0 publications

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

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