Ripeness Evaluation of Achacha Fruit Using Hyperspectral Image Data
نویسندگان
چکیده
In this study, spectral data within the wavelength range of 400–780 nm were used to evaluate ripeness stages achacha fruits. The status fruits was divided into seven stages. Both average and pixel-based approaches assess ripeness. accuracy n-level-error each stage predicted by using classification models (Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Artificial Neural Network (ANN) K-Nearest Neighbor (KNN)) regression (Partial Regression (PLSR) Support (SVR)). Furthermore, how curvature fruit surface affected prediction investigated. With use an averaged spectrum samples, model in study ranged from 52.25% 79.75%, one-level error (94.75–100%) much higher. SVM had highest (79.75%), PLSR (100%). results majority rule, (58.25–79.50%) one-level-error (95.25–99.75%) all comparable with spectrum. showed that could have a noticeable effect on evaluation values low or high stage. Thus, central region would be relatively reliable choice for evaluation. For fruit, value face exposed sunlight one level higher than shadow. when close mid-value two adjacent values, chance having errors. sorting practical postharvest processing
منابع مشابه
Hyperspectral Image Analysis for Measuring Ripeness of Tomatoes
The latest developments in optics and sensors allow imaging spectrometry: the creation of hyperspectral images, i.e. images with wavelength-specific measurements over a large part of the spectrum. In this study, hyperspectral images of several stages of ripeness of tomatoes were recorded and analyzed. The electromagnetic spectrum between 450 and 850 nm was recorded in 80 bands (every 5 nm). Res...
متن کاملDestriping of hyperspectral image data: an evaluation of different algorithms using EO-1 Hyperion data
Data from the Earth Observing-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensor-internal sources of errors. These include diffuse sensor noise, striping, smile-effect, keystone effect, and spatial misalignments between the detector arrays. For ...
متن کاملComparative Evaluation of Image Fusion Methods for Hyperspectral and Panchromatic Data Fusion in Agricultural and Urban Areas
Nowadays remote sensing plays a key role in the field of earth science studies due to some of the advantages, including data collection at a very low cost and time on a very large scale. Meanwhile, using hyperspectral data is of great importance due to the high spectral resolution. Because of some limitations, such as hyperspectral imaging technology, it suffers from a reduction in the spatial ...
متن کاملhazard evaluation of gas condensate stabilization and dehydration unit of parsian gas refinery using hazop procedures
شناسایی مخاطرات در واحد 400 پالایشگاه گاز پارسیان. در این پروزه با بکارگیری از تکنیک hazop به شناسا یی مخاطرات ، انحرافات ممکن و در صورت لزوم ارایه راهکارهای مناسب جهت افزایش ایمنی فرا یند پرداخته میگردد. شرایط عملیاتی مخاطره آمیز نظیر فشار و دمای بالا و وجود ترکیبات مختلف سمی و قابل انفجار در واحدهای پالایش گاز، ضرورت توجه به موارد ایمنی در این چنین واحدهایی را مشخص می سازد. مطالعه hazop یک ر...
Predicting of the Quality Attributes of Orange Fruit Using Hyperspectral Images
Background: Hyperspectral image analysis is a fast and non-destructive technique that is being used to measure quality attributes of food products. This research investigated the feasibility of predicting internal quality attributes, such as Total Soluble Solids (TSS), pH, Titratable Acidity (TA), and maturity index (TSS/TA); and external quality attributes such as color components (L*, a*, b*)...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Agriculture
سال: 2022
ISSN: ['2077-0472']
DOI: https://doi.org/10.3390/agriculture12122145