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.

In the realm of agricultural automation, the efficient management of tasks like yield estimation, harvesting, and monitoring is crucial. While fruits are typically detected using bounding boxes, pixel-level segmentation is essential for extracting detailed information such as color, maturity, and shape. Furthermore, while previous studies have typically focused on controlled environments and scenes, achieving robust performance in real orchard conditions is also imperative. To prioritize these aspects, we propose the following two considerations: first, a novel peach image dataset designed for rough orchard environments, focusing on pixel-level segmentation for detailed insights; and second, utilizing a transformer-based instance segmentation model, specifically the Swin Transformer as a backbone of Mask R-CNN. We achieve superior results compared to CNN-based models, reaching 60.2 AP on the proposed peach image dataset. The proposed transformer-based approach specially excels in detecting small or obscured peaches, making it highly suitable for practical field applications. The proposed model achieved 40.4 AP for small objects, nearly doubling that of CNN-based models. This advancement significantly enhances automated agricultural systems, especially in yield estimation, harvesting, and crop monitoring.

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.

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.

This study was conducted to determine the optimal irrigation starting point by analyzing tree growth, physiological responses, fruit quality, and productivity in peach orchards. Seven-year-old 'Kawanakajima Hakuto' peach trees were used in an experimental field (35°49′30.4″N, 127°01′33.2″E) located within the National Institute of Horticultural and Herbal Science located in Wanju-gun, Jeollabuk-do. The irrigation starting point was set with four levels of-20,-40,-60, and-80 kPa from June to September 2022. While there were no significant differences in increase of trunk cross-section area and leaf area among treatments, shoot length and diameter decreased in the-80 kPa and-20 kPa treatments. The photosynthetic rate measured in August was highest for-60 kPa (17.7 μmol•m-2 •s-1), followed by-40 kPa (15.6 μmol•m-2 •s-1),-20 kPa (14.5 μmol•m-2 •s-1) and-80 kPa (14.0 μmol•m-2 •s-1). SPAD value measured in May and August was lower in the-80 kPa and-20 kPa treatments than in the-60 kPa and-40 kPa treatments. The harvest date reached three days earlier in the-20 kPa treatment compared to other treatments. The fruit weight was highest in the-60 kPa (379.1 g), followed by-40 kPa (344.0 g),-80 kPa (321.0 g) and-20 kPa (274.9 g). Firmness was the lowest in the-20 kPa treatment. The soluble solid content was highest in the-60 kPa treatment (13.3°Bx).The ratio of marketable fruits was highest in the-60 kPa treatment (50.7%) and lowest in the-80 kPa treatment (23.4%). In conclusion, we suggest that setting the irrigation starting point at-60 kPa could improve the fruit quality and yield in peach orchards.