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Climate thresholds and yield elasticity of durian, mangosteen, and coffee under hydroclimatic variability in Thailand
(2026-08-01)
Ansari, Kutubuddin
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Tanır Kayıkçı, Emine
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Jamjareegulgarn, Punyawi
This study examines the empirical relationships between hydroclimatic variability and the yields of durian, mangosteen, and coffee across Thailand using meteorological observations from 56 stations and provincial level yield statistics for 2008 to 2024. An integrated statistical framework was applied, including exploratory correlation analysis, nonlinear quadratic additive modelling, lagged climate response analysis, elasticity estimation, model comparison, mangosteen regional sensitivity analysis, and a Climate Risk Index (CRI). The results show clear spatial gradients in temperature, humidity, and precipitation across the six agroclimatic regions of Thailand, broadly corresponding to regional crop productivity patterns. Annual anomaly analysis indicates that warm and humid years are generally associated with higher durian and mangosteen yields, whereas excessive rainfall and warming are associated with reduced coffee productivity. Pairwise linear correlations between annual climate variables and crop yields are generally weak, suggesting that simple linear models may not fully capture crop climate relationships. Nonlinear modelling indicates approximate empirical temperature turning points near 28.0°C for durian, 27.3°C for mangosteen, and 26.6°C for coffee, with humidity related turning points around 76–78%. Lagged climate response models suggest that multi-year hydroclimatic conditions may influence perennial crop productivity, particularly humidity for durian, precipitation for coffee, and temperature and precipitation for mangosteen. The mangosteen sensitivity analysis shows that humidity and precipitation thresholds are affected by regional composition, especially when marginal production regions are included. The CRI formulation under the equal weight, mangosteen shows the highest rainfall related climate risk signal, while coffee is more sensitive to temperature related risk and durian shows moderate vulnerability to prolonged humid and wet conditions.
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Interpretable ANN-Based Computer Vision System for Mangosteen Ripeness Detection for Export Markets
(2026-01-21)
Lapcharoensuk, Ravipat
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Tosribunjerd, Naphon
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Poonpakdee, Pasu
Mangosteen is a high-value tropical fruit widely consumed and exported from Thailand. Mangosteen ripeness classification is crucial for export quality control, but manual grading leads to inconsistency and inefficiency. This study presents a computer vision system using an Artificial neural network to classify mangosteen into ripe, semi-ripe, and unripe stages based on peel color. A dataset of 378 images was collected and processed to extract 40 color-based features across multiple color spaces. Principal Component Analysis demonstrated non-linear separability among the ripeness classes. SMOTE and Gaussian noise augmentation were used to tackle data imbalance and enhance generalizability. The model reached a 95% accuracy rate and displayed flawless precision and recall for the ripe class. Integrated Gradients analysis highlighted the importance of the red-green color component (CIELAB a*) in the classification process. The proposed method demonstrates a low-cost, interpretable, and efficient solution suitable for real-world application in the mangosteen export industry.
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Surface roughness classification of mangosteen with gray level co-occurrence matrix based texture analysis
(2018-07-02)
Acharya, Anjali
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Phothisonothai, Montri
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Tantisatirapong, Suchada
Mangosteen is one of the fruits that has an enormous export potential in Thailand. It is well-known as the queen of fruit. Mangosteen export generates large revenue; however, fruit is not defect free it contains many undesirable external as well as internal condition which results in the shipment rejection and decrease the reliability of the export. Therefore, this research investigates an approach for texture image analysis based surface roughness detection and classification into 3 classes: i.e., Glossy Surface, Mid Rough Surface and Extreme Rough Surface. In this study, for the first time, we propose the textural features extracted using Gray-Level Co-occurrence Matrix (GLCM) for surface roughness classification of mangosteen.
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Minimally destructive assessment of mangosteen translucency based on electrical impedance measurements
(2016-02-01)
Nakawajana, Natrapee
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Terdwongworakul, Anupun
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Teerachaichayut, Sontisuk
Electrical impedance spectroscopy in a frequency range of 1 kHz-200 kHz was studied to develop a classifying model for translucent mangosteen. The optimal configuration of the measurement was investigated. Transverse alignment of two measuring needles with the stem-calyx axis and with the measured position on the part of the pericarp pertinent to the largest flesh segment proved to be the optimal configuration. The optimal electrical parameters were selected at frequencies of 1, 4, 7, 8, 14, 47, 73, and 81 kHz as the classifying variables based on the student t-test analysis for a significant difference between the normal and translucent mangosteen and the largest difference of the average values of the electrical parameters. The differences in the electrical parameters and their reciprocals were the optimal classifying variables. The model constructed from the samples from two seasons was robust in terms of seasonality, providing a classification accuracy of 82.7%. The difference in the initial moisture content of the pericarp was justifiably compensated by the differences in the electrical parameters. The EIS technique was suitable for measurement of mangosteen samples at the maturity color stage in which the sample contained no yellow latex in the pericarp.
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Detection of hardening pericarp disorder and determination of firmness at hardening area in mangosteen by visible-near infrared reflectance spectroscopy
(2015-01-01)
Workhwa, S.
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Teerachaichayut, S.
Mangosteen (Garcinia mangostona L.) is an economically important fruit grown commercially in Thailand for domestic consumption and export. The fruit has a thick and hard pericarp. However, hardening pericarp disorder can easily occur as a result of compression or impact during harvest and transport. Classification and prediction of hardening pericarp disorder in mangosteen was investigated using visible-near infrared spectroscopy (Vis/NIRS). Reflectance spectra were acquired on each of 1100 mangosteen samples. The number of samples for training and test set was 733 and 367 samples, respectively. Partial least squares-discriminant analysis (PLS-DA) was used for quantitative analysis. The results of discriminant analysis of normal and hardening pericarp samples using leave-one-out cross-validation achieved an average total accuracy of 92.92%. A further goal was quantitative analysis of firmness of mangosteens with hardening pericarp using Vis/NIR measurements. The optimum calibration model was pretreated using standard normal variate transformation (SNV) pretreatment and was developed using partial least squares regression (PLSR). The model was proven useful for prediction of the degree of pericarp hardening of mangosteen. The coefficients of correlation (R) and root mean square error of cross validation (RMSECV) were 0.89 and 2.67N respectively. This technique has potential use for nondestructive and rapid classification of quality for mangosteen.
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Determination of translucent content in mangosteen by means of near infrared transmittance
(2012-03-01)
Terdwongworakul, Anupun
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Nakawajana, Natrapee
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Teerachaichayut, Sontisuk
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Janhiran, Athit
Translucent flesh disorder is undesirable in mangosteen meant for export. However, mangosteens are judged as translucent when the translucent flesh is visible on the pulp surface regardless of the quantity of the internal translucent flesh which may result in some mangosteen assessed as normal having the same amount of translucent flesh content as a mangosteen judged as translucent. The critical amount of translucent flesh to be visible on the pulp surface needs to be determined for assessment purposes. A non-destructive technique to measure the translucent content is a practical tool as the first step towards the establishment of the critical value. A non-destructive model was developed to estimate the translucent content in mangosteens using near infrared transmittance. The translucent area of the flesh section on the fruit surface was used to indicate the translucent content. The effects of the orientation of the fruit and also of the light source to the relative position of the detector as well as the effect of the measurement position of the fruit on the predictive performance were examined. The results showed that the best partial least squares model was achieved with spectra acquired from the fruit position which revealed the largest flesh segment (prediction correlation coefficient was 0.86 and root mean square error of prediction was 7.58%). The horizontal stem-calyx fruit axis and a 135° angle from the light source relative to the detector were the optimal fruit orientation and configuration for measurement. © 2011 Elsevier Ltd. All rights reserved.
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Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy
(2011-10-01)
Teerachaichayut, Sontisuk
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Terdwongworakul, Anupun
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Thanapase, Warunee
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Kiji, Kazuaki
A non-destructive technique to predict a hardening pericarp disorder in intact mangosteen is proposed by using near infrared (NIR) transmittance spectroscopy in the wavelength range of 660-960 nm. The study found that the spectral features of normal pericarp mangosteen and hardening pericarp mangosteen were different. The averaged spectra and individual spectra of hardening pericarp mangosteen from a calibration set (N = 560) were used to develop classification models, using partial least squares discriminant analysis (PLS-DA). A model based on individual spectra obtained better classification. The overall accuracy of classification for a prediction set (N = 358) was 91%. Out of 179 samples of normal pericarp fruits, 167 were identified correctly, while 159 samples out of 179 samples with hard pericarp were predicted correctly. The results showed that NIR transmittance spectroscopy can be used to predict hard pericarp disorder in intact mangosteen fruit accurately. © 2011 Elsevier Ltd. All rights reserved.
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Bioactive properties of Snake fruit (Salacca edulis Reinw) and Mangosteen (Garcinia mangostana) and their influence on plasma lipid profile and antioxidant activity in rats fed cholesterol
(2006-09-01)
Leontowicz, Hanna
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Leontowicz, Maria
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Drzewiecki, Jerzy
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Haruenkit, Ratiporn
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Poovarodom, Sumitra
Two exotic fruits (Snake fruit and Mangosteen) were characterized by polyphenols, proteins and antioxidant potentials and by their influence on plasma lipids and antioxidant activity in rats fed cholesterol. The content of polyphenols (14.9±1.5 and 9.2±0.8 mg GAE g<sup>-1</sup>) and antioxidant potential (46.7±4.7 and 72.9±7.4 μmol TE g <sup>-1</sup>) in Snake fruit was significantly higher than in Mangosteen (P<0.05). Twenty male Wistar rats were divided into four dietary groups: Control, Chol, Chol/Snake and Chol/Mangosteen. After 4 weeks of the experiment diets supplemented with Snake fruit and to a lesser degree with Mangosteen significantly hindered the rise in plasma lipids and hindered a decrease of antioxidant activity. Changes were found in fibrinogen fraction, such as solubility and mobility by the number of protein bands detected in SDS-electrophoresis: Chol/Snake differed from Chol/Mangosteen. In conclusion, Snake fruit and Mangosteen contain high quantity of bioactive compounds, therefore positively affect plasma lipid profile and antioxidant activity in rats fed cholesterol-containing diets. Such positive influence is higher in rats fed diet with added Snake fruit.

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