Jung Gun Cho

This study was conducted to analyze the quality changes of 'Hongro' apples during different stages of growth and to establish optimal harvest indices for determining maturity, as well as to set quality standards for mechanical harvesting. Seven-year-old 'Hongro' apple trees were used in an experimental field located within the National Institute of Horticultural and Herbal Science in Wanju-gun, Jeollabuk-do. From 2021 to 2023, fruit quality was assessed at 10-day intervals, starting from 58-59 days after full bloom (early June) to 164-173 days after full bloom (late September), past the typical harvest period. Additionally, quality data collected from Wanju-gun, Hwaseong, and Pocheon were used to analyze regional variations in the quality of 'Hongro' apples. The fruit weight increased as growth progressed, reaching 250-320g just before the harvest period. Firmness and acidity decreased as the fruit matured, while soluble solids content increased but plateaued between 12-14°Brix. The sugar-to-acid ratio increased with fruit maturity, surpassing 50 at harvest. The L* and b* color values increased initially but dropped sharply near harvest, while the a* value increased rapidly at harvest. Starch content declined significantly during the later stages of growth, falling below 20mg•g-1 at harvest.The correlation analysis between starch content and various fruit characteristics demonstrated a significant positive relationship with both firmness and acidity, yielding correlation coefficients of 0.86 and 0.84, respectively. Consequently, firmness and acidity were identified as optimal indices for determining maturity. The established minimum quality standards for harvest readiness were as follows: firmness at 39.7±6.4N, acidity at 0.255±0.052%, soluble solids content at 13.6±1.3°Brix, and a* value of fruit skin at 14.7±12.8. These quality criteria, combined with the high correlation coefficients observed in non-destructive measurement parameters(such as fruit length, width, and a* color value) are anticipated to facilitate efficient mechanical harvesting while also offering critical information to growers, enabling them to optimize harvest timing and potentially improve economic benefits.

In South Korea, the prevalence of red coloration in peach flesh has been widely documented in recent years owing to elevated temperatures triggered by climate change. This study implemented soil-fruit-daylight-system (SFDS) artificial chambers that maintained four distinct day and night temperature categories to provide an overview of the effects of high temperatures on flesh discoloration and reduced fruit quality in 'Daewol' peaches. The day and night temperatures in each chamber were maintained as 1; 20°C/10°C; 2; 25°C/15°C; 3; 30°C/20°C; and 4; 35°C/25°C, while all other external factors remained constant. Changes in flesh color and the physicochemical quality characteristics related to flesh were assessed under these conditions over two consecutive years, 2022 and 2023. The significantly lowest fruit weight was observed in the 30°C/20°C and 35°C/25°C chambers, where tropical night phenomena were demonstrated. A remarkably higher soluble solids content was reported from the 35°C/25°C chamber, with firmness however not showing a significant difference among the chambers. Peach flesh displayed excessive red/brown coloration in the 30°C/20°C and 35°C/25°C chambers, exhibiting higher a * values, lower hue values, and color development index (CDI) values beyond 0.5 ± 0.01, where 0.5 signifies maximum yellowness. Furthermore, the markedly highest reddish pulp index was observed in the chambers with temperatures set to 30°C/20°C and 35°C/25°C. These findings emphasize that reddish pulp disorder intensifies with elevated temperatures as a result of climate change. Hence, it is crucial to prioritize and address this issue to maintain a steady peach production process and improve peach quality levels ultimately to meet the demand for peach fruit. This requires the prompt adoption of strategies such as appropriate irrigation, shading methods to protect the fruits from intense sunlight, and choosing peach varieties that can withstand high temperatures.

In the agricultural sector, crop classification serves as a crucial indicator for evaluating the adaptability of crops influenced according to factors such as climate, soil conditions, and altitude. In this context, this study aims to analyze the accuracy of crop classification models based on spectral index extraction methods using multi-spectral satellite imagery for apple and pear crops. For this purpose, multi-spectral images were acquired for Nonsan city in Chungcheongnam-do, and apple and pear crops were classified according to the main spectral index extraction methods. The results showed that a machine learning algorithm, support vector machine (SVM), yielded higher accuracy when using the top 50% of the spectral indices (R 2 = 0.7487) compared to the use of all spectral indices. Additionally, a deep learning model, multi-layer perceptron (MLP), showed relatively little variation in accuracy based on the spectral index extraction method (R 2 = 0.8902). Therefore, the findings of this study present an efficient and scientific spectral index extraction method for classifying various crops, including apples and pears, across a wide range.

Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery acquired with an unmanned aerial vehicle, for the management of nutrients in an apple orchard. The k-nearest neighbors regression (KNR) model for Ch and all macronutrients was chosen as the best model through a comparison of calibration and validation R² values. As a result of model development, a total of 13 band ratios (425/429, 682/686, 710/714, 714/718, 718/722, 750/754, 754/758, 758/762, 762/766, 894/898, 898/902, 906/911, and 963/967) were selected for Ch and all macronutrients. The estimation potential for the T-N and Mg concentrations was low, with an R² ≤ 0.37. The estimation performance for the other macronutrients was as follows: R² ≥ 0.70 and RMSE ≤ 1.43 μg/cm² for Ch; R² ≥ 0.44 and RMSE ≤ 0.04% for P; R² ≥ 0.53 and RMSE ≤ 0.23% for K; R² ≥ 0.85 and RMSE ≤ 6.18% for C; and R² ≥ 0.42 and RMSE ≤ 0.25% for Ca. Through establishing a fertilization strategy using the macronutrients estimated through hyperspectral imagery and measured soil chemical properties, this study presents a nutrient management decision-making method for apple orchards.

In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors.

Peaches are one of the most popular fruits around the globe. Selecting the optimum harvesting maturity for peaches is crucial in assuring high-quality fruits. This study is a model for determining the ideal harvest time for a robot harvester. Our study was carried out over two years on ‘Mihong’ peaches during days after full bloom (DAFB) 71 to 90 in 2021 and DAFB 64 to 84 in 2022 to select the optimal maturity index through a quality survey. The fruit size, soluble solids content (SSC), titratable acidity (TA), firmness, peel color (L*, a*, b*, chroma, and hue), and ethylene production were investigated. Fruit size showed the regular double sigmoid curve, and SSC increased while firmness and TA decreased with time. The samples left in storage conditions in 2022 showed a massive change in SSC and firmness after DAFB 74, implying the optimum harvesting stage. Interestingly, color values manifest the same consequence with a*, b*, and hue by reaching a plateau with apex side color values after DAFB 74, indicating the desired maturity. Overall, the results show that color values are an outstanding non-destructive alternative to typical destructive measurements for determining the exact time to harvest ‘Mihong’ peaches.

We investigated the effects of elevated temperatures on the photosynthetic characteristics and fruit quality of the ‘Mihong’ peach (Prunus persica) under high carbon dioxide concentrations based on climate change scenario RCP 8.5. We simulated three different temperature conditions (control; the average temperature in normal years in Jeonju city, C+3.4 °C, C+5.7 °C) and 700 μmol·mol⁻¹ CO₂ (expected in the mid-21st century). The average photosynthetic rates were 10.64, 10.21, and 8.18 μmol∙m⁻²∙s⁻¹ at C+3.4 °C, control, and C+5.7 °C, respectively. The chlorophyll content in the control and C+3.4 °C increased compared to that at the early stage of growth, but that of C+5.7 °C decreased rapidly. The fruit weight was the highest for C+3.4 °C (158.2 g), followed by C+5.7 °C (129.5 g) and the control (127.3 g). The shoot length increased at C+3.4 °C and C+5.7 °C when compared to the control, and there was no significant difference in the other growth characteristics. The carbohydrate content was the highest at C+3.4 °C, and the nitrogen content increased as the temperature increased. Moreover, the C/N ratio decreased as the temperature increased but there was no significant difference. The rate of floral bud differentiation decreased as temperature increased, and that of C+5.7 °C was the lowest due to the low C/N ratio, vegetative growth dominance, and early defoliation. Furthermore, the floral bud density was low in the C+5.7 °C treatment. These findings indicate that a temperature increase of 5.7 °C under 700 μmol·mol⁻¹ CO₂ conditions will negatively affect the physiological response and fruit quality of the early-maturing peach variety ‘Mihong,’ and yield is expected to decrease in the following year.

Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R²) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R² values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R² = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.