Search Results (56)

Strawberries have a short shelf-life leading to food loss and waste when production unexpectedly exceeds demand. PGRs may have potential to delay production and reduce food loss and waste, but no PGRs are available for delaying strawberry production. The aim of this preliminary study was to investigate re-purposing a stomatal blocking film antitranspirant polymer as a PGR to temporarily delay production. Poly-1-p-menthene or water was applied during early fruit ripening in two glasshouse experiments, one on a June-bearer cultivar and one on an everbearer cultivar. Ripe strawberries were harvested during the next 23 days, the cumulative yield was recorded, and the production curves were fitted using polynomial regression in groups. The statistical analysis showed that cubic polynomial regression curves could be fitted separately to each treatment. Application of the blocker delayed the production of both cultivars by 1–2 days during the period of rapid berry production. The delay diminished and cumulative yield returned to the water-treated value by 13 and 18 days after application in the June-bearer and everbearer cultivars, respectively. At 23 days after application, the blocker gave 8% greater cumulative yield in the June-bearer, but not in the everbearer. It was concluded that, if a greater delay could be achieved, there may be potential to use stomatal blockers as PGRs in some cultivars of strawberry to delay production and reduce food loss and waste when unanticipated lower demand occurs. Full article

Strawberry post-harvest losses are estimated at 50%, due to improper handling and harvest timing, necessitating the use of non-invasive methods. This study develops a non-invasive in situ bioelectrical spectroscopy for strawberry peduncles. Based on traditional assessments and invasive metrics, 100 physiologically ripe (PR) and 100 commercially mature (CM) strawberries were distinguished. Spectra from their peduncles were measured from 1 kHz to 1 MHz, collecting four parameters (magnitude (Z(f)), phase angle (θ(f)), resistance (R(f)), and reactance (X(f))), resulting in 80,000 raw data points. Through systematic spectral preprocessing, Bode and Cole–Cole plots revealed a distinction between PR and CM strawberries. Frequency selection identified seven key frequencies (1, 5, 50, 75, 100, 250, 500 kHz) for deriving 37 engineered features from spectral, extrema, and derivative parameters. Feature selection reduced these to 6 parameters: phase angle at 50 kHz (θ (50 kHz)); relaxation time (τ); impedance ratio (|Z_1k/Z_250k|); dispersion coefficient (α); membrane capacitance (Cm); and intracellular resistivity (ρi). Four algorithms (TabPFN, CatBoost, GPC, EBM) were evaluated with Monte Carlo cross-validation with five iterations, ensuring robust evaluation. CatBoost achieved the highest accuracy at 93.3% ± 2.4%. Invasive reference metrics showed strong correlations with bioelectrical parameters (r = 0.74 for firmness, r = −0.71 for soluble solids). These results demonstrate a solution for precise harvest classification, reducing post-harvest losses without compromising marketability. Full article

Strawberry (Fragaria × ananassa), a nutrient-dense fruit with significant economic value in commercial cultivation, faces critical detection challenges in automated harvesting due to complex growth conditions such as foliage occlusion and variable illumination. To address these limitations, this study proposes Ripe-Detection, a novel lightweight object detection framework integrating three key innovations: a PEDblock detection head architecture with depth-adaptive feature learning capability, an ADown downsampling method for enhanced detail perception with reduced computational overhead, and BiFPN-based hierarchical feature fusion with learnable weighting mechanisms. Developed using a purpose-built dataset of 1021 annotated strawberry images (Fragaria × ananassa ‘Red Face’ and ‘Sachinoka’ varieties) from Changchun Xiaohongmao Plantation and augmented through targeted strategies to enhance model robustness, the framework demonstrates superior performance over existing lightweight detectors, achieving mAP50 improvements of 13.0%, 9.2%, and 3.9% against YOLOv7-tiny, YOLOv10n, and YOLOv11n, respectively. Remarkably, the architecture attains 96.4% mAP50 with only 1.3M parameters (57% reduction from baseline) and 4.4 GFLOPs (46% lower computation), simultaneously enhancing accuracy while significantly reducing resource requirements, thereby providing a robust technical foundation for automated ripeness assessment and precision harvesting in agricultural robotics. Full article

The external appearance of strawberry fruits serves as a critical criterion for their commercial value and grading standards. However, current research primarily emphasizes ripeness and surface defects, with limited attention given to the quantitative analysis of geometric characteristics such as deformity and symmetry. To address this gap, this study proposes a comprehensive evaluation framework that integrates deep learning-based segmentation with geometric analysis for strawberry appearance quality assessment. First, an enhanced YOLOv11 segmentation model incorporating a Squeeze-and-Excitation attention mechanism was developed to enable high-precision extraction of individual fruits, achieving Precision, Recall, AP₅₀, and F1 scores of 91.11%, 87.46%, 92.90%, and 88.45%, respectively. Second, a deformity quantification method was designed based on the number of deformity points (N_d), deformity rate (R_d), and spatial distance metrics (G_min and G_max). Experimental results demonstrated significant differences in R_d and G_max between deformed and normal strawberries, indicating strong classification capability. Finally, principal component analysis (PCA) was employed to extract the primary axis direction, and morphological symmetry was quantitatively evaluated using Intersection over Union (IoU) and Area Difference Ratio (AreaD_Ratio). The results revealed that most samples fell within an IoU range of 0.6–0.8 and AreaD_Ratio below 0.4, indicating noticeable inter-individual differences in fruit symmetry. This study aims to establish a three-stage analytical framework—segmentation, deformity quantification, and symmetry evaluation—for assessing strawberry appearance quality, with the goal of supporting key applications in automated grading and precision quality inspection. Full article

An enhanced YOLOv8-based network was developed to accurately and efficiently detect the ripeness of strawberries in complex environments. Firstly, a CA (channel attention) mechanism was integrated into the backbone and head of the YOLOv8 model to improve its ability to identify key features of strawberries. Secondly, the bilinear interpolation operator was replaced with DySample (dynamic sampling), which optimized data processing, reduced computational load, accelerated upsampling, and improved the model’s sensitivity to fine strawberry details. Finally, the Wise-IoU (Wise Intersection over Union) loss function optimized the IoU (Intersection over Union) through intelligent weighting and adaptive tuning, enhancing the bounding box accuracy. The experimental results show that the improved YOLOv8-CDW model has a precision of 0.969, a recall of 0.936, and a mAP@0.5 of 0.975 in complex environments, which are 8.39%, 18.63%, and 12.75% better than those of the original YOLOv8, respectively. The enhanced model demonstrates higher accuracy and faster detection of strawberry ripeness, offering valuable technical support for advancing deep learning applications in smart agriculture and automated harvesting. Full article

This study investigated the impact of cultivar, harvest time, and ripening stage of strawberries on their aroma concentration and profile, and colour stability of nectars produced from these strawberries. Purees from 12 different cultivars from two countries, collected at different ripening stages and harvest times, were analysed. Furaneol and mesifuran content was analysed using a gas chromatography–flame ionisation detector (GC-FID), and gas chromatography–mass spectrometry (GC-MS) was used to determine the content of 12 aroma compounds, including esters, C6 compounds, and lactones. Nectars produced from these purees had their colour stability measured over 12 weeks. Both the colour and aroma were greatly influenced by strawberry cultivar. Within cultivars, nectars produced from strawberries that had been harvested overripe showed higher colour stability and higher concentrations of aroma compounds than those harvested ripe from an earlier harvest, although some cultivars were more affected by harvest time than ripening stage. Aroma compounds that correlated significantly (p < 0.05) with a good colour after storage included furaneol, ethyl butanoate, hexanal, γ-decalactone and γ-dodecalactone, as well as the total concentration of aroma compounds. Only γ-decalactone concentrations correlated significantly with overall nectar colour stability, although this could be due to cultivar effects. Full article

Automated ripeness detection in large-scale strawberry cultivation is often challenged by complex backgrounds, significant target scale variation, and small object size. To address these problems, an efficient strawberry ripeness detection model, YOLOv11-HRS, is proposed. This model incorporates a hybrid channel–space attention mechanism to enhance its attention to key features and to reduce interference from complex backgrounds. Furthermore, the RepNCSPELAN4_L module is devised to enhance multi-scale target representation through contextual feature aggregation. Simultaneously, a 160 × 160 small-target detection head is embedded in the feature pyramid to enhance the detection capability of small targets. It replaces the original SPPF module with the higher-performance SPPELAN module to further enhance detection accuracy. Experimental results on the self-constructed strawberry dataset SRD show that YOLOv11-HRS improves mAP@0.5 and mAP@0.5:0.95 by 3.4% and 6.3%, respectively, reduces the number of parameters by 19%, and maintains a stable inference speed compared to the baseline YOLOv11 model. This study presents an efficient and practical solution for strawberry ripeness detection in natural environments. It also provides essential technical support for advancing intelligent management in large-scale strawberry cultivation. Full article

With the development of intelligent modern agriculture, strawberry harvesting robots play an increasingly important role in precision agriculture. However, existing vision systems face multiple challenges in complex farmland environments, including fruit occlusion, difficulties in recognizing fruits at varying ripeness levels, and limited real-time processing capabilities. This study proposes a keypoint detection and 3D localization method for strawberry fruits utilizing a depth camera to address these challenges. By introducing a Haar Wavelet Downsampling (HWD) module and Gold-YOLO neck, the proposed method achieves significant improvements in feature extraction and detection performance. The integration of the HWD module effectively reduces image noise, enhances feature extraction accuracy, and strengthens the method’s ability to recognize fruit stems. Additionally, incorporating the Gold-YOLO neck structure enhances multi-scale feature fusion, improving detection accuracy and enabling the method to adapt to complex environments. To further accelerate inference speed and enable deployment in an embedded system, Layer-adaptive sparsity for Magnitude-based Pruning (LAMP) technology is employed, significantly reducing redundant parameters and thereby enhancing the lightweight performance of the model. Experimental results demonstrate that the proposed method can accurately identify strawberries at different ripeness stages and exhibits strong robustness under various lighting conditions and complex scenarios, achieving an average precision of 97.3% while reducing model parameters to 38.2% of the original model, significantly improving the efficiency of strawberry fruit localization. This method provides robust technical support for the practical application and widespread adoption of agricultural robots. Full article

Strawberries are highly perishable fruits harvested at full ripeness, and their nutritional quality together with their phytochemical composition can be significantly affected by storage duration and temperature. This study investigated the changes in key bioactive compounds, including folate, vitamin C, anthocyanins, quercetin-3-glucoside, ellagic acid, and organic acids, in “Red Rhapsody” strawberries stored at two typical household temperatures (4 °C and 23 °C). While storage duration and temperature did not have a significant impact (p > 0.05) on folate content, significant changes in other phytochemicals were observed. The total anthocyanin content increased significantly (p < 0.05), from 30.0 mg/100 g fresh weight (FW) at Day 0 to 84.4 mg/100 g FW at Day 7 at 23 °C, a 2.8-fold increase. Conversely, the vitamin C content was significantly reduced (p < 0.05), from 54.1 mg/100 g FW at Day 0 to 28.4 mg/100 g FW at Day 7 at 23 °C, while it remained stable at 4 °C. Additionally, the concentrations of quercetin-3-glucoside, ellagic acid, and organic acids underwent significant changes during the storage period. The total folate content fluctuated between 73.2 and 81.6 μg/100 g FW at both temperatures. These results suggest that storage temperature and duration influence the individual phytochemicals and nutrients of strawberries differently, with potential implications for their nutritional value and bioactive compound content. Full article

The existing strawberry ripeness detection algorithm has the problems of a low precision and a high missing rate in real complex scenes. Therefore, we propose a novel model based on a hybrid attention mechanism. Firstly, a partial convolution-based compact inverted block is developed, which significantly enhances the feature extraction capability of the model. Secondly, an efficient partial hybrid attention mechanism is established, which realizes the remote dependence and accurate localization of strawberry fruit. Meanwhile, a multi-scale progressive feature pyramid network is constructed, and the fine-grained features of strawberry targets of different sizes are accurately extracted. Finally, a Focaler-shape-IoU loss function is proposed to effectively solve the problem of the difficulty imbalance between strawberry samples and the influence of the shape and size of the bounding box on the regression. The experimental results show that the model’s precision and mAP0.5 reach 92.1% and 92.7%, respectively, which are 2.0% and 1.7% higher than the baseline model. Additionally, our model is better in detection performance than most models with fewer parameters and lower FLOPs. In summary, the model can accurately identify the maturity of strawberry fruit under complex farmland environments and provide certain technical guidance for automated strawberry-picking robots. Full article

In the last decade, significant efforts have been made to predict sensory characteristics using electronic senses, such as the electronic nose (e-nose) and the electronic tongue (e-tongue), and discuss their relationship to the eating quality evaluated by human panels. This study was conducted (1) to characterize the aroma and taste profiles of strawberries over a 5-day storage period (4 °C) using both electronic senses and human panels and (2) to correlate the electronic sense data with human panel data. A total of 10 sensory attributes of strawberries, including 7 aroma and 3 taste attributes, were analyzed by a descriptive sensory panel (n = 16) over the five days. Although the human panel did not find significant differences in the intensities of the strawberry attributes over the five days, the intensity ratings showed an increasing or decreasing trend over the storage period. However, the e-nose and the e-tongue discriminated each of the storage days of the strawberry samples. Furthermore, the partial least square regression coefficients of determination (R²) indicated that the e-nose and the e-tongue were highly predictive in their evaluation of the intensities of all the descriptive sensory attributes. Lastly, the concentrations of furaneol, one of the key volatiles imparting a distinct ripe strawberry aroma, were determined using an e-nose to correlate with the intensities of aroma attributes evaluated by the panel. A significant positive Pearson’s correlation coefficient was found with the intensities of overripe aroma. The findings indicate the potential of electronic senses to determine sensory characteristics and their excellent capability to predict the eating quality of strawberries. Full article

This paper presents the design of a strawberry fruit head gripper unit, together with the concept of a control system for the operation of its mechanisms and vision system. The developed design consists of three specialised mechanisms: positioning, grasping, and cutting off of the fruit. A Finite Element Method (FEM) model was developed for the described design. Next, calculations were carried out, based on which the construction materials were selected. The key performance parameters of the functional model, built on the basis of the developed design concept, were verified under laboratory conditions. In tests carried out on the possible hematoma caused by exceeding the breaking stress induced by the pressure of the encompassing jaws on the fruit, it was found that none of the fruit tested suffered mechanical damage as a result of the sensor triggering force, and the average length of the trimmed stalk was approximately 14 mm. The designed head gripper, together with the proposed automation system, will contribute to improving harvesting precision, and this will favour a reduction in the quantitative and qualitative losses of the harvested crop. The experimental tests conducted under harvesting conditions showed a high efficiency of 95% in identifying ripe fruit, and the harvesting efficiency of the robotic arm was 90%. Full article

Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of low detection accuracy and slow detection speed in the assessment of strawberry fruit maturity in orchards, a CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced. The composite thinning network, CRNet, is utilized for target fusion, employing multi-branch blocks to enhance images by restoring high-frequency details. To address the issue of low computational efficiency in the multi-head self-attention (MHSA) model due to redundant attention heads, the design concept of CGA is introduced. This concept aligns input feature grouping with the number of attention heads, offering the distinct segmentation of complete features for each attention head, thereby reducing computational redundancy. A hybrid operator, ACmix, is proposed to enhance the efficiency of image classification and target detection. Additionally, the Inner-IoU concept, in conjunction with Shape-IoU, is introduced to replace the original loss function, thereby enhancing the accuracy of detecting small targets in complex scenes. The experimental results demonstrate that CR-YOLOv9 achieves a precision rate of 97.52%, a recall rate of 95.34%, and an mAP@50 of 97.95%. These values are notably higher than those of YOLOv9 by 4.2%, 5.07%, and 3.34%. Furthermore, the detection speed of CR-YOLOv9 is 84, making it suitable for the real-time detection of strawberry ripeness in orchards. The results demonstrate that the CR-YOLOv9 algorithm discussed in this study exhibits high detection accuracy and rapid detection speed. This enables more efficient and automated strawberry picking, meeting the public’s requirements for food safety. Full article

In agriculture, the timely and accurate assessment of fruit ripeness is crucial to optimizing harvest planning and reduce waste. In this article, we explore the integration of two cutting-edge deep learning models, YOLOv9 and Swin Transformer, to develop a complex model for detecting strawberry ripeness. Trained and tested on a specially curated dataset, our model achieves a mean precision (mAP) of 87.3% by using the metric intersection over union (IoU) at a threshold of 0.5. This outperforms the model using YOLOv9 alone, which achieves an mAP of 86.1%. Our model also demonstrated improved precision and recall, with precision rising to 85.3% and recall rising to 84.0%, reflecting its ability to accurately and consistently detect different stages of strawberry ripeness. Full article

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible–near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500–978 nm) and NIR (908–1676 nm) data collected from 180 samples of “Tochigi iW1 go” white strawberries and 150 samples of “Tochigi i27 go” red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R²_p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R²_p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system. Full article