Hyperspectral imaging technigue for monitoring moisture content of
                    blueberry during the drying process

Choi, Ji‐Young; Kim, Jiyoon; Kim, Jungsoo; Jeong, Saeul; Kim, Minhyun; Park, Sanghyeok; Moon, Kwang-Deog

doi:10.11002/kjfp.2021.28.4.445

Cited by 4 publications

(5 citation statements)

References 42 publications

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“…In this study, the spectral data obtained through contact-based methods allowed for rapid and bulk data collection compared to conventional physiological measurement methods. In the future, there is a need to utilize non-contact methods such as hyperspectral imaging to improve and support blueberry cultivation [73][74][75][76]. Currently, the high cost and difficulty in portability of equipment present limitations, and individual measurements in the field are challenging.…”

Section: Discussionmentioning

confidence: 99%

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

Jeong,

Jang,

Kim

et al. 2024

Agronomy

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While crushed pinecone mulch holds promise as a beneficial material for blueberry cultivation, research on its effectiveness remains limited. Crop leaf characteristics can serve as parameters for assessing mulching effects, although there are several limitations, including the need to analyze various distinct characteristics separately. The combination of hyperspectral data and machine learning techniques is expected to enable the selection of only the most important features among these characteristics. In this study, we investigated the impact of various mulching treatments utilizing pine tree byproducts, including crushed pinecones. Mulching variations included non-mulching (NM), crushed pinecones (PCs), a mixture of crushed pinecones and sulfur (PCS), pine needles (PNs), and sulfur treatment (S). Conventional methods were employed to measure leaf growth (length and width) and physiological characteristics (chlorophyll content, chlorophyll fluorescence, and stomatal conductance). Hyperspectral reflectance was also measured, and classification models using Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting (XGBoost) were developed for crop characteristics, vegetation indices (VIs), visible and near-infrared (VNIR), and short-wave infrared (SWIR). The results showed that using crushed pinecones as the sole mulching material for blueberries, without sulfur treatment, had a positive impact on blueberry growth. The PC treatment exhibited a dual effect on plant growth by lowering the soil pH to 5.89 and maintaining soil moisture within the range of 26.33–35.20%. We observed distinct differences in soil inorganic nutrient content, with higher concentrations of organic matter, total nitrogen, and available P2O5 and K+, which positively influenced blueberry growth. Mulching treatments demonstrated superior physiological characteristics, with two classification models identifying stomatal conductance (gs) as a key parameter influencing treatment classification (VIP scores > 1 rank: 3, variable score rank: 1). The photochemical reflectance index (PRI) emerged as a major parameter among VIs, showing potential for measuring water stress (VIP scores > 1 rank: 2, variable score rank: 1). In the SWIR PLS-DA model, wavelength peaks were mainly observed in the O-H overtone (1410 nm, 1450 nm, 1930 nm, 1940 nm, and 2100 nm). Overall, crushed pinecones were found to positively impact the initial growth of blueberries by enhancing water status (plant respiration).

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Section: Discussionmentioning

confidence: 99%

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

Jeong,

Jang,

Kim

et al. 2024

Agronomy

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“…The smoothing method replaces a specific range of values with a mean value to represent a noisy spectrum as a smooth curve. In addition, pre-processing by SNV and MSC can eliminate the light scattering effect due to irregular shapes and sizes [ 34 ].…”

Section: Processes On the Spectramentioning

confidence: 99%

“…The regression coefficients were high at 706, 790, 827, 868 and 894 nm, and these wavelengths were introduced as the best wavelength for the prediction of MC. The two pre-processing methods of SVN and MSC, along with the PLS model, were the best models for MC prediction [ 34 ]. The researchers recommended using hyperspectral imaging techniques to predict the MC of blueberries during drying.…”

Section: Comparison Of Various Techniques For Prediction Of Moisture ...mentioning

confidence: 99%

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

2023

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Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.

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“…Additionally, recent studies have also shown that HSI is feasible for visualizing the distribution of internal quality in fruits, such as SSC and PH distribution of cherries, mandarins and kiwifruit [12][13][14], and firmness of peach [15]. Beyond these examples, research using hyperspectral imaging and chemometrics has shown promising results in maturity analysis and quality evaluation of blueberries, persimmons, bananas, and other fruits [16][17][18][19]. The above researches mainly focus on the prediction of single indicators of SSC and hardness during the sample maturity stages.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Wang,

Yang,

et al. 2024

Horticulturae

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Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

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Hyperspectral imaging technigue for monitoring moisture content of blueberry during the drying process

Cited by 4 publications

References 42 publications

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

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