Variety Classification of Lactuca Sativa Seeds Using Single-Kernel RGB Images and Spectro-Textural-Morphological Feature-Based Machine Learning

Concepcion, Ronnie; Lauguico, Sandy; Siphengphet, Khamsoy; Alejandrino, Jonnel; Dadios, Elmer P.

doi:10.1109/hnicem51456.2020.9400015

Cited by 13 publications

(8 citation statements)

References 28 publications

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“…The seed size in this study was manually measured using a digital calliper. In the future, it will be possible to measure seed size to determine the quality/purity of seeds/genetics by machine learning more accurately without damaging the [32,[36][37][38][39]. Different letters indicate significant differences between means within columns at Tukey's 5% level of significance The weight of 1000 grains of chili seeds ranges from 3.68 -6.71 g (Table 3).…”

Section: Discussionmentioning

confidence: 99%

Performance and seed quality of several open pollinated chili seed lots

Azmi

Sembiring²,

Manik

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Information related to the performance of chili plants and seeds is important for seed industry players to know. Therefore, observations were made regarding the performance and quality of Open Pollinated chili varieties seeds from Indonesian Vegetables Research Institute which has an excellent opportunity to be licensed by private seed producers in Indonesia. Seeds of seven OP chili varieties produced in 2021 were observed for seed performance and quality. Data were analyzed descriptively. The analysis showed that each variety’s seed performance was different, while the seed quality achieved not much different. Physical purity, moisture content and seed germination ranged from 100%, 3.8-7.0% and 76-98%, respectively. Ciko variety has the largest seed size and 1000-seed weight compared to other varieties. While Lembang 1 variety has the smallest seed size and 1000-seed weight.

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Section: Discussionmentioning

confidence: 99%

Performance and seed quality of several open pollinated chili seed lots

Azmi

Sembiring²,

Manik

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Low-cost adaptation for LAI using mobile phone camera is reported in several articles [32,33,38]. The developed fisheye camera method (DHP) has been extensively implemented together with plant canopy analyser.…”

Section: Vegetation Indices Extraction 221 Leaf Area Index (Lai)mentioning

confidence: 99%

“…The developed fisheye camera method (DHP) has been extensively implemented together with plant canopy analyser. Figure 3 depicts the LAI-Mobile instrument, which utilises a fisheyes lens clipped onto a mobile phone [38]. Prior to capturing the canopy hemispheric image, the mobile phone is affixed to a selfie stick.…”

Section: Vegetation Indices Extraction 221 Leaf Area Index (Lai)mentioning

confidence: 99%

“…What is Measured What is indicated Techniques Applied Leaf area index (LAI) [38] canopy hemispheric image plant growth condition and healthy status 1. fisheyes lens clipped onto a mobile phone 2. the mobile phone is affixed to a selfie stick Leaf spot area index (LSAI) [33] spot area to the leaf area health of a tree Use a black self-stick board with a 2cmby 2cm white square is for image rectification and calibration purposes Normalized difference vegetation index (NDVI) [9] The NIR value reflected by the leaf plant health 1. Use a digital camera with no embedded NIR-blocking filter 2.…”

Section: Vegetation Indicesmentioning

confidence: 99%

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Reviewing Vegetation Indices for Mobile Application: Potentials and Challenges

Puteri Suhaiza Sulaiman,

Fatimah Khalid,

Azreen Azman

et al. 2023

ARASET

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Vegetation are quantitative metrics that are used to assess the density of vegetation as well as its health and vitality. Vegetation indices are important in precision agriculture and monitoring environmental conditions. For years, vegetation indices have been derived from remote sensing or aerial imaging data, with focus on large plantation estate and green coverage. Recently, there is a growing interest in using mobile phone for measuring vegetation indices at a more affordable cost. The spectral range for vegetation indices extends from 400 nm to 900 nm, which places it in the VIS and NIR spectral categories, respectively. The CMOS sensor that is typically found in mobile phone cameras has the capacity to capture NIR images. This paper aims to give a background in understanding vegetation indices available and weighing the suitability of using these indices for mobile applications. The potential of using these indices has been establish when the studies indicate several works have successfully used mobile phone camera to capture vegetation indices formulation, but still additional features is needed to ensure the standardisation and consistency of image calibration.

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“…The authors of [10] discussed The classification of lactuca sativa seeds into different varieties is a complex task that has been attempted in the past through the use of various image-processing techniques. In this study, we use a machine-learning algorithm to classify lactuca sativa seeds into different varieties based on their spectral features.…”

mentioning

confidence: 99%

Literature Survey on Grain Type Prediction Using Machine Learning

Reddy¹,

Gupta²,

Raj³

et al. 2023

IJRASET

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Classification of grains according to their type becomes very much necessary because there are multiple grains available in the market today. Classification of Grains according their types manually is not efficient. Classification becomes a really wearisome task when it comes to doing it hand-operated instead of automating the process. Manually classifying would be a grueling task. There is a need for mentally keen and quick system which can get better of this difficulty by automating these processes. It should be able to recognize and label the every single grain according to their respective type automatically. Therefore we conducted a literature survey on how various seeds were classified and various analysis which were used to obtain the various desired result. Here we observed that various researchers used k-means, CNN and various other algorithms.

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Variety Classification of Lactuca Sativa Seeds Using Single-Kernel RGB Images and Spectro-Textural-Morphological Feature-Based Machine Learning

Cited by 13 publications

References 28 publications

Performance and seed quality of several open pollinated chili seed lots

Performance and seed quality of several open pollinated chili seed lots

Reviewing Vegetation Indices for Mobile Application: Potentials and Challenges

Literature Survey on Grain Type Prediction Using Machine Learning

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