Research

16 pages, 951 KiB

Open AccessArticle

Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning

by Ewa Ropelewska, Dorota E. Kruczyńska, Ahmed M. Rady, Krzysztof P. Rutkowski, Dorota Konopacka, Karolina Celejewska and Monika Mieszczakowska-Frąc

Agriculture 2023, 13(3), 562; https://doi.org/10.3390/agriculture13030562 - 25 Feb 2023

Viewed by 1666

Abstract

Dried red-fleshed apples are considered a promising high-quality product from the functional foods category. The objective of this study was to compare the flesh features of freeze-dried red-fleshed apples belonging to the ‘Alex Red’, ‘Trinity’, ‘314’, and ‘602’ genotypes and indicate which parameters [...] Read more.

Dried red-fleshed apples are considered a promising high-quality product from the functional foods category. The objective of this study was to compare the flesh features of freeze-dried red-fleshed apples belonging to the ‘Alex Red’, ‘Trinity’, ‘314’, and ‘602’ genotypes and indicate which parameters and shapes of dried samples are the most useful to distinguish apple genotypes. Apple samples were at the stage of harvest maturity. The average fruit weight, starch index, internal ethylene concentration, flesh firmness, total soluble sugar content, and titratable acidity were determined. One hundred apple slices with a thickness of 4 mm and one hundred cubes with dimensions of 1.5 cm × 1.5 cm × 1.5 cm of each genotype were subjected to freeze-drying. For each apple sample (slice or cube), 2172 image texture parameters were extracted from images in 12 color channels, and color parameters L*, a*, and b* were determined. The classification models were developed based on a set of selected image textures and a set of combined selected image textures and color parameters of freeze-dried apple slices and cubes using various traditional machine-learning algorithms. Models built based on selected textures of slice images in 11 selected color channels correctly classified freeze-dried red-fleshed apple genotypes with an overall accuracy reaching 90.25% and mean absolute error of 0.0545; by adding selected color parameters (L*, b*) to models, an increase in the overall accuracy to 91.25% and a decrease in the mean absolute error to 0.0486 were observed. The classification of apple cube images using models including selected texture parameters from images in 11 selected color channels was characterized by an overall accuracy of up to 74.74%; adding color parameters (L*, a*, b*) to models resulted in an increase in the overall accuracy to 80.50%. The greatest mixing of cases was observed between ‘Alex Red’ and ‘Trinity’ as well as ‘314’ and ‘602’ apple slices and cubes. The developed models can be used in practice to distinguish freeze-dried red-fleshed apples in a non-destructive and objective manner. It can avoid mixing samples belonging to different genotypes with different chemical properties. Further studies can focus on using deep learning in addition to traditional machine learning to build models to distinguish dried red-fleshed apple samples. Moreover, other drying techniques can be applied, and image texture parameters and color features can be used to predict the changes in flesh structure and estimate the chemical properties of dried samples. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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13 pages, 1559 KiB

Open AccessArticle

The Classification of Peaches at Different Ripening Stages Using Machine Learning Models Based on Texture Parameters of Flesh Images

by Ewa Ropelewska and Krzysztof P. Rutkowski

Agriculture 2023, 13(2), 498; https://doi.org/10.3390/agriculture13020498 - 20 Feb 2023

Cited by 2 | Viewed by 2787

Abstract

The ripening stage can affect consumer preference and the market value of peaches. This study was aimed at developing an objective, effective, and fast procedure for distinguishing the different stages of ripeness of peaches using image texture parameters and models built using traditional [...] Read more.

The ripening stage can affect consumer preference and the market value of peaches. This study was aimed at developing an objective, effective, and fast procedure for distinguishing the different stages of ripeness of peaches using image texture parameters and models built using traditional machine learning algorithms. The ripeness classes (distinguished using DA-Meter-based nondestructive VIS/NIR method) 0.1, 0.4, and 0.9 for ‘Redhaven’ peaches and 0.1, 0.4, and 1.0 for ‘Royal Glory’ peaches were considered. Fruit weight, ethylene production, total soluble solids content (SSC), titratable acidity (TA), and fruit firmness (FF) were measured. The slice images for each class were acquired. Selected texture parameters from images in color channels R, G, B, L, a, b, X, Y, and Z were used to develop classification models for distinguishing peach ripening stages in pairs. Models were built for combined textures selected from images in all color channels, individual color spaces, and individual color channels using various machine learning algorithms. The ethylene production and SSC was higher in peaches with a ripeness class of 0.1 than in less ripe fruit. The least ripe fruit of ‘Redhaven’ and ‘Royal Glory’ peaches were characterized by the highest fruit firmness. Furthermore, statistically significant differences in SSC between classes 0.1 and 0.9 of ‘Redhaven’ were observed. For ‘Royal Glory’, statistically significant differences in TA were determined between all classes. These differences may be related to classification performance metrics. In the case of ‘Redhaven’ peaches, two extreme ripeness classes 0.1 (the greatest ripeness) and 0.9 (the least ripeness) were correctly classified with the highest accuracy reaching 100% for models built based on textures selected from all color channels (random forest and Bayes net algorithms) and color space lab (random forest). For individual color channels, the accuracy reached 99% for color channel G (random forest) and color channel a (logistic). The accuracy of classifying ripening stages 0.1 and 0.4 reached 98% for the model built using textures from all color channels and color space lab (Bayes net). The ripening stages 0.4 and 0.9 were distinguished with an accuracy of up to 96% (all color channels, random forest). The classification of ripening stages of ‘Royal Glory’ peaches reached 100% for all pairs, 0.1 vs. 1.0 (all color channels, color spaces RGB, color space lab, color channel G, color channel a), 0.1 vs. 0.4 (all color channels, color space RGB, color space lab), and 0.4 vs. 1.0 (all color channels). The developed procedure can be useful in practice. Distinguishing peaches at different stages of ripeness and the selection of fruit at the optimal stage can be important for consumption and processing. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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9 pages, 591 KiB

Open AccessArticle

Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.)

by Younés Noutfia and Ewa Ropelewska

Agriculture 2023, 13(1), 26; https://doi.org/10.3390/agriculture13010026 - 22 Dec 2022

Cited by 6 | Viewed by 1656

Abstract

The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, and ‘Bousthammi’ date varieties, converted to individual color channels, [...] Read more.

The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, and ‘Bousthammi’ date varieties, converted to individual color channels, were processed to extract the texture parameters. After performing the attribute selection, the textures were used to build models intended for the discrimination of different varieties of date palm fruit using machine learning algorithms from Functions, Bayes, Lazy, Meta, and Trees groups. Models were developed for combining image textures selected from a set of all color channels and for sets of textures selected for individual color spaces and color channels. The models, including combined textures selected from all color channels, distinguished all five varieties with an average accuracy reaching 98%, and ‘Bousthammi’ and ‘Mejhoul’ were completely correctly discriminated for the SMO (Functions) and IBk (Lazy) machine learning algorithms. By reducing the number of varieties, the correctness of the date palm fruit classification increased. The models developed for the three most different date palm fruit varieties ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ revealed an average discrimination accuracy of 100% for each algorithm used (SMO, Naive Bayes (Bayes), IBk, LogitBoost (Meta), and LMT (Trees)). In the case of individual color spaces and channels, the accuracies were lower, reaching 97.3% for color space RGB and SMO and LMT algorithms for all five varieties and 99.63% for Naive Bayes and IBk for the ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ date palm fruits. The results can be used in practice to develop vision systems for sorting and distinguishing the varieties of date palm fruit to authenticate the variety of the fruit intended for further processing. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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17 pages, 2629 KiB

Open AccessArticle

New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment

by Miguel Noguera, Borja Millan and José Manuel Andújar

Agriculture 2023, 13(1), 4; https://doi.org/10.3390/agriculture13010004 - 20 Dec 2022

Cited by 2 | Viewed by 4507

Abstract

The state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development [...] Read more.

The state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development and poor responsiveness for growers. These limitations have shifted the focus to remote-sensing, spectroscopy-based approaches. However, most of the research focusing on these approaches has been accomplished with expensive equipment, which is exorbitant for most users. To combat this issue, this work presents a low-cost, hand-held, multispectral device with original hardware specially designed to face the complexity related to in-field use. The proposed device is based on a development board (AS7265x, AMS AG) that has three sensor chips with a spectral response of eighteen channels in a range from 410 to 940 nm. The proposed device was evaluated in a red-grape field experiment. Briefly, it was used to acquire the spectral signature of eighty red-grape samples in the vineyard. Subsequently, the grape samples were analysed using standard chemical methods to generate ground-truth values of ripening status indicators (soluble solid content (SSC) and titratable acidity (TA)). The eighteen pre-process reflectance measurements were used as input for training artificial neural network models to estimate the two target parameters (SSC and TA). The developed estimation models were evaluated through a leave-one-out cross-validation approach obtaining promising results (R² = 0.70, RMSE = 1.21 for SSC; and R² = 0.67, RMSE = 0.91 for TA). Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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16 pages, 4408 KiB

Open AccessArticle

Ripeness Evaluation of Achacha Fruit Using Hyperspectral Image Data

by Ngo Minh Tri Nguyen and Nai-Shang Liou

Agriculture 2022, 12(12), 2145; https://doi.org/10.3390/agriculture12122145 - 13 Dec 2022

Cited by 6 | Viewed by 3449

Abstract

In this study, spectral data within the wavelength range of 400–780 nm were used to evaluate the ripeness stages of achacha fruits. The ripeness status of achacha fruits was divided into seven stages. Both average and pixel-based approaches were used to assess the [...] Read more.

In this study, spectral data within the wavelength range of 400–780 nm were used to evaluate the ripeness stages of achacha fruits. The ripeness status of achacha fruits was divided into seven stages. Both average and pixel-based approaches were used to assess the ripeness. The accuracy and n-level-error accuracy of each ripeness stage was predicted by using classification models (Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN)) and regression models (Partial Least Square Regression (PLSR) and Support Vector Regression (SVR)). Furthermore, how the curvature of the fruit surface affected the prediction of the ripeness stage was investigated. With the use of an averaged spectrum of fruit samples, the accuracy of the model used in this study ranged from 52.25% to 79.75%, and the one-level error accuracy (94.75–100%) was much higher. The SVM model had the highest accuracy (79.75%), and the PLSR model had the highest one-level error accuracy (100%). With the use of pixel-based ripeness prediction results and majority rule, the accuracy (58.25–79.50%) and one-level-error accuracy (95.25–99.75%) of all models was comparable with the accuracy predicted by using averaged spectrum. The pixel-based prediction results showed that the curvature of the fruit could have a noticeable effect on the ripeness evaluation values of achacha fruits with a low or high ripeness stage. Thus, using the spectral data in the central region of achacha fruits would be a relatively reliable choice for ripeness evaluation. For an achacha fruit, the ripeness value of the fruit face exposed to sunlight could be one level higher than that of the face in shadow. Furthermore, when the ripeness value of achacha fruit was close to the mid-value of two adjacent ripeness stage values, all models had a high chance of having one-level ripeness errors. Thus, using a model with high one-level error accuracy for sorting would be a practical choice for the postharvest processing of achacha fruits. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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18 pages, 3264 KiB

Open AccessArticle

Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression

by Pimpen Pornchaloempong, Sneha Sharma, Thitima Phanomsophon, Kraisuwit Srisawat, Wasan Inta, Panmanas Sirisomboon, Witoon Prinyawiwatkul, Natrapee Nakawajana, Ravipat Lapcharoensuk and Sontisuk Teerachaichayut

Agriculture 2022, 12(12), 2060; https://doi.org/10.3390/agriculture12122060 - 30 Nov 2022

Cited by 6 | Viewed by 3066

Abstract

Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality [...] Read more.

Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r²) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r², a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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13 pages, 1788 KiB

Open AccessArticle

Consensual Regression of Lasso-Sparse PLS models for Near-Infrared Spectra of Food

by Lei-Ming Yuan, Xiaofeng Yang, Xueping Fu, Jiao Yang, Xi Chen, Guangzao Huang, Xiaojing Chen, Limin Li and Wen Shi

Agriculture 2022, 12(11), 1804; https://doi.org/10.3390/agriculture12111804 - 29 Oct 2022

Cited by 1 | Viewed by 1410

Abstract

In some cases, near-infrared spectra (NIRS) make the prediction of quantitative models unreliable, and the choice of a suitable number of latent variables (LVs) for partial least square (PLS) is difficult. In this case, a strategy of fusing member models with important information [...] Read more.

In some cases, near-infrared spectra (NIRS) make the prediction of quantitative models unreliable, and the choice of a suitable number of latent variables (LVs) for partial least square (PLS) is difficult. In this case, a strategy of fusing member models with important information is gradually becoming valued in recent research. In this work, a series of PLS regression models were developed with an increasing number of LVs as member models. Then, the least absolute shrinkage and selection operator (Lasso) was employed as the model’s selection access to sparse uninformative ones among these PLS member models. Deviation weighted fusion (DW-F), partial least squares regression coefficient fusion (PLS-F), and ridge regression coefficient fusion (RR-F) were comparatively used further to fuse the above sparsed member models, respectively. Three spectral datasets, including six attributes in NIR data of corn, apple, and marzipan, respectively, were applied in order to validate the feasibility of this fusion algorithm. Six fusion models of the above attributes performed better than the general optimal PLS model, with a noticeable enhancement of root mean errors squared of prediction (RMSEP) arriving at its highest at 80%. It also reduced more than half of the spectral bands; the DW-F especially showed its excellent fusing capacity and obtained the best performance. Results show that the preferred strategy of DW-F model combined with Lasso selection can make full use of spectral information, and significantly improve the prediction accuracy of fusion models. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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11 pages, 1736 KiB

Open AccessArticle

The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line

by Kadir Sabanci, Muhammet Fatih Aslan, Vanya Slavova and Stefka Genova

Agriculture 2022, 12(10), 1652; https://doi.org/10.3390/agriculture12101652 - 09 Oct 2022

Cited by 2 | Viewed by 1791

Abstract

The objective of this study was to evaluate differences between the red onion cultivar and breeding line using models based on selected fluorescence spectroscopic data built using machine-learning algorithms from different groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The combination of [...] Read more.

The objective of this study was to evaluate differences between the red onion cultivar and breeding line using models based on selected fluorescence spectroscopic data built using machine-learning algorithms from different groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The combination of fluorescence spectroscopy and machine learning is an original approach to the non-destructive and objective discrimination of red onion samples. The selected fluorescence spectroscopic data were used to build models using algorithms from the groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The most satisfactory results were obtained using J48 and LMT (Logistic Model Tree) from the group of Trees, Multilayer Perceptron, and QDA (Quadratic Discriminant Analysis) from Functions, Naive Bayes from Bayes, Logit Boost from Meta, JRip from Rules, and LWL (Locally Weighted Learning) from Lazy. The average accuracy of discrimination of onion bulbs belonging to ‘Asenovgradska kaba’ and a red breeding line equal to 100% was found in the case of models developed using the LMT, Multilayer Perceptron, Naive Bayes, Logit Boost, and LWL algorithms. The TPR (True Positive Rate), Precision, and F-Measure of 1.000 and FPR (False Positive Rate) of 0.000, as well as the Kappa statistic of 1.0, were determined. The results revealed the usefulness of the approach combining fluorescence spectroscopy and machine learning to distinguish red onion cultivars and breeding lines. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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13 pages, 2053 KiB

Open AccessArticle

Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas

by Khouloud Abida, Meriem Barbouchi, Khaoula Boudabbous, Wael Toukabri, Karem Saad, Habib Bousnina and Thouraya Sahli Chahed

Agriculture 2022, 12(9), 1429; https://doi.org/10.3390/agriculture12091429 - 09 Sep 2022

Cited by 10 | Viewed by 2421

Abstract

Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning [...] Read more.

Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum Distance Classification (MDC). According to our research, the RF classification provided a better result than other classification models. RF classification exhibited the best values of overall accuracy, kappa, recall, precision and RMSE, with 99.54%, 0.98%, 0.98%, 0.98% and 0.23%, respectively. However, low precision was observed for the MDC method (RMSE = 1.15). The results were more intriguing since they highlighted the value of the bare soil index as a covariate for LU mapping. Our results suggest that Sentinel-2 combined with RF classification is efficient for creating a LU map. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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17 pages, 4816 KiB

Open AccessArticle

Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation

by Ranbing Yang, Yuming Zhai, Jian Zhang, Huan Zhang, Guangbo Tian, Jian Zhang, Peichen Huang and Lin Li

Agriculture 2022, 12(9), 1363; https://doi.org/10.3390/agriculture12091363 - 01 Sep 2022

Cited by 9 | Viewed by 2046

Abstract

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth [...] Read more.

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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16 pages, 11344 KiB

Open AccessArticle

Prediction Model for Tea Polyphenol Content with Deep Features Extracted Using 1D and 2D Convolutional Neural Network

by Na Luo, Yunlong Li, Baohua Yang, Biyun Liu and Qianying Dai

Agriculture 2022, 12(9), 1299; https://doi.org/10.3390/agriculture12091299 - 25 Aug 2022

Cited by 3 | Viewed by 1774

Abstract

The content of tea polyphenols (TP) is one of the important indicators for judging the quality of tea. Accurate and non-destructive estimation technology for tea polyphenol content has attracted more and more attention, which has become a key technology for tea production, quality [...] Read more.

The content of tea polyphenols (TP) is one of the important indicators for judging the quality of tea. Accurate and non-destructive estimation technology for tea polyphenol content has attracted more and more attention, which has become a key technology for tea production, quality identification, grading and so on. Hyperspectral imaging technology is a fusion of spectral analysis and image processing technology, which has been proven to be an efficient technology for predicting tea polyphenol content. To make full use of spectral and spatial features, a prediction model of tea polyphenols based on spectral-spatial deep features extracted using convolutional neural network (CNN) was proposed, which not only broke the limitations of traditional shallow features, but also innovated the technical path of integrated deep learning in non-destructive detection for tea. Firstly, one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN) models were constructed to extract the spectral deep features and spatial deep features of tea hyperspectral images, respectively. Secondly, spectral deep features, spatial deep features, and spectral-spatial deep features are used as input variables of machine learning models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF). Finally, the training, testing and evaluation were realized using the self-built hyperspectral dataset of green tea from different grades and different manufacturers. The results showed that the model based on spectral-spatial deep features had the best prediction performance among the three machine learning models (R² = 0.949, MAE = 0.533 for training sets, R² = 0.938, MAE = 0.799 for test sets). Moreover, the visualization of estimation results of tea polyphenol content further demonstrated that the model proposed in this study had strong estimation ability. Therefore, the deep features extracted using CNN can provide new ideas for estimation of the main components of tea, which will provide technical support for the estimation tea quality estimation. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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12 pages, 1425 KiB

Open AccessArticle

Establishment of Non-Destructive Methods for the Detection of Amylose and Fat Content in Single Rice Kernels Using Near-Infrared Spectroscopy

by Shuang Fan, Zhuopin Xu, Weimin Cheng, Qi Wang, Yang Yang, Junyao Guo, Pengfei Zhang and Yuejin Wu

Agriculture 2022, 12(8), 1258; https://doi.org/10.3390/agriculture12081258 - 19 Aug 2022

Cited by 2 | Viewed by 2074

Abstract

For the efficient selection of high-quality rice varieties, the near-infrared spectroscopy (NIRS) technique has been widely applied to detect constituents in single rice kernels. Compared with other constituents, amylose content (AC) and fat content (FC) are the key parameters that can affect the [...] Read more.

For the efficient selection of high-quality rice varieties, the near-infrared spectroscopy (NIRS) technique has been widely applied to detect constituents in single rice kernels. Compared with other constituents, amylose content (AC) and fat content (FC) are the key parameters that can affect the quality of rice. Based on two modified AC and FC trace detection methods, two NIRS methods to detect AC and FC in single rice kernels were developed. Using the proposed methods, the AC and FC in two groups of rice kernel datasets were measured. The datasets were collected on two spectrometers with different sample movement states (static and dynamic) and measurement modes (diffuse reflectance (NIRr) and diffuse transmission (NIRt)). By optimizing the pre-treatment method and spectral range, the determination coefficients of cross-validation (R²_cv) and prediction (R²_p) of the NIRS models under different measurement conditions were all above 0.6. The results indicated that the proposed methods were applicable to the rapid, non-destructive detection and sorting of individual rice seeds with different AC and FC, and it was shown that these methods can meet the requirements of the rough screening of rice seed varieties. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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17 pages, 3248 KiB

Open AccessArticle

Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

by Bingru Hou, Yaohua Hu, Peng Zhang and Lixia Hou

Agriculture 2022, 12(7), 897; https://doi.org/10.3390/agriculture12070897 - 21 Jun 2022

Cited by 7 | Viewed by 2984

Abstract

Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The [...] Read more.

Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The determination of disease severity was accomplished by two methods directly or indirectly based on differences in reflectance. One approach was to utilize ML algorithms to build a model that directly reflects the relationship between disease level and spectral reflectance. Another method was to first use partial least squares to construct a predictive model of internal physicochemical values, such as relative chlorophyll content (SPAD) and peroxidase (POD) activity, and then use an ML model to classify disease levels based on the predicted values. The classification accuracy based on these two methods could reach up to 99 and 95%, respectively. The changes in physicochemical values during the development of disease were further investigated. Regression models for fitting changes in SPAD value and POD activity were developed based on temperature and incubation time, with determination coefficients of 0.961 and 0.997, respectively. The prediction of epidemic period was realized by combining regression and classification models based on physicochemical values with an accuracy of 88.5%. It is demonstrated that rapid non-destructive determination of physicochemical values based on Vis/NIR spectroscopy for potato late blight detection is feasible. Furthermore, it is possible to guide the control of disease throughout the epidemic period. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products)

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