White strawberries are high-value agricultural products whose ripeness identification poses a notable challenge for robotic harvesting. This study first evaluated YOLOv5 and YOLOv12 for vision-based ripeness detection, using conventional strawberries as a control group. Results showed that both algorithms yielded substantially lower mAP scores on white strawberries than on conventional ones, with a maximum of only 68.20%, confirming the inadequacy of current image-based approaches. To overcome this limitation, industrial-grade infrared cameras were employed to acquire infrared images and gray-scale intensity curves from six groups of white and conventional strawberries under laboratory and field conditions. Validation through peak-contrast gray-scale analysis, least-squares Gaussian fitting, linear discriminant analysis, and principal component analysis demonstrated that the infrared gray-scale curve method reliably differentiated ripeness stages for both strawberry types, outperforming conventional visual recognition. These findings provide a practical basis for automated harvesting and ripeness assessment systems.
AriasF.AppelmannT.GiraldoL. F. G. (2020). Import and export of strawberries in a global market. Ciencias económicas y contables, desafíos y retos para la competitividad; SepúlvedaJA, Ed, 280–291.
2.
ZhouHWangXAuW, et al.Intelligent robots for fruit harvesting: recent developments and future challenges. Precision Agriculture2022; 23: 1856–1907.
3.
WangZXunYWangY,et al.Review of smart robots for fruit and vegetable picking in agriculture. Int J Agr Biol Eng2022; 15: 33–54.
4.
OliveiraLFMoreiraAPSilvaMF. Advances in agriculture robotics: a state-of-the-art review and challenges ahead. Robotics2021; 10: 52.
5.
YangSWangWGaoS,et al.Strawberry ripeness detection based on YOLOv8 algorithm fused with LW-Swin transformer. Comput Electron Agric2023; 215: 108360.
6.
HaoCDongWLiH, et al.YOLO-AMAS: maturity detection of ‘Jiang’Pomegranate in Complex orchard environments. Agriculture2025; 15: 2514.
7.
VazquezDVSpetaleFENankarAN, et al.Machine learning-based tomato fruit shape classification system. Plants2024; 13: 2357.
8.
ZhaoSFangCHuaT,et al.Detecting the maturity of red strawberries using improved YOLOv8 s model. Agriculture2025; 15: 2263.
9.
LiRJiZHuS, et al.Tomato maturity recognition model based on improved YOLOv5 in greenhouse. Agronomy2023; 13: 03.
10.
AzarmdelHJahanbakhshiAMohtasebiSS,et al.Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biol Technol2020; 166: 111201.
11.
PengKMaWLuJ, et al.Application of machine vision technology in citrus production. Appl Sci2023; 13: 9334.
12.
PhanQHNguyenVTLienCH, et al.Classification of tomato fruit using yolov5 and convolutional neural network models. Plants2023; 12: 90.
13.
AzadniaRFouladiSJahanbakhshiA. Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques. Results in Engineering2023; 17: 100891.
14.
WangC.WangH.HanQ., et al. (2024). Strawberry detection and ripeness classification using yolov8 + Model and image processing method. Agriculture, 14, 51.
15.
ChenSXiongJJiaoJ, et al.Citrus fruits maturity detection in natural environments based on convolutional neural networks and visual saliency map. Precision Agriculture2022; 23: 1515–1531.
16.
AzarmdelHJahanbakhshiAMohtasebiSS,et al.Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biol Technol2020; 166: 111201.
17.
AlfatniMSMKhairunniza-BejoSMarhabanMHB, et al.Towards a real-time oil palm fruit maturity system using supervised classifiers based on feature analysis. Agriculture2022; 12: 1461.
18.
LuJSangNHuY,et al.Detecting citrus fruits with highlight on tree based on fusion of multi-map. Optik (Stuttg)2014; 125: 1903–1907.
19.
MhangoJKGroveIGHartleyW, et al.Applying colour-based feature extraction and transfer learning to develop a high throughput inference system for potato (Solanum tuberosum L.) stems with images from unmanned aerial vehicles after canopy consolidation. Precision Agriculture2022; 23: 643–669.
20.
RopelewskaERutkowskiKP. The classification of peaches at different ripening stages using machine learning models based on texture parameters of flesh images. Agriculture2023; 13: 98.
21.
WangDWangXChenY, et al.Strawberry ripeness classification method in facility environment based on red color ratio of fruit rind. Comput Electron Agric2023; 214: 108313.
22.
TangCChenDWangX, et al.A fine recognition method of strawberry ripeness combining mask R-CNN and region segmentation. Front Plant Sci2023; 14: 1211830.
23.
FanL.YuJ.ZhangP., et al. (2024). Prediction of strawberry quality during maturity based on hyperspectral technology. Agronomy, 14, 1450.
24.
KhanamRHussainM. What is YOLOv5: A deep look into the internal features of the popular object detector. arXiv preprint arXiv:2407.20892. https://doi.org/10.48550/arXiv.2407.20892 (2024).
EspinozaSAguileraCRojasL,et al.Analysis of fruit images with deep learning: a systematic literature review and future directions. IEEE Access2023; 12: 3837–3859.
27.
LiYXueJZhangM, et al.YOLOv5-ASFF: a multistage strawberry detection algorithm based on improved YOLOv5. Agronomy2023; 13: 1901.
28.
FanLYuJZhangP,et al.Prediction of strawberry quality during maturity based on hyperspectral technology. Agronomy2024; 14: 1450.
29.
SimkovaKVebericRHudinaM, et al.Non-destructive and destructive physical measurements as indicators of sugar and organic acid contents in strawberry fruit during ripening. Sci Hortic2024; 327: 112843.
30.
SuZZhangCYanT, et al.Application of hyperspectral imaging for maturity and soluble solids content determination of strawberry with deep learning approaches. Front Plant Sci2021; 12. doi: 10.3389/fpls.2021.736334
31.
AmiDNatalelloAMereghettiP, et al.FT-IR spectroscopy supported by PCA–LDA analysis for the study of embryonic stem cell differentiation. Journal of Spectroscopy2010; 24: 89–97.
32.
LasalviaMCapozziVPernaG. A comparison of PCA-LDA and PLS-DA techniques for classification of vibrational spectra. Appl Sci2022; 12: 5345.
33.
ZhaoF.SongP.ZhangX., et al. (2021). Identification of candidate genes influencing anthocyanin biosynthesis during the development and ripening of red and white strawberry fruits via comparative transcriptome analysis. PeerJ, 9, e10739.
