Page 95 - IJAMD-2-2

International Journal of AI for
Materials and Design Fruit image detection using AI

based technologies in fruit production systems. Appl Fruit 32. Moya V, Quito A, Pilco A, Vásconez JP, Vargas C. Crop
Sci. 2025;67(1):1-18. detection and maturity classification using a yolov5-based
image analysis. Emerg Sci J. 2024;8(2):496-512.
doi: 10.1007/s10341-024-01223-4
doi: 10.28991/ESJ-2024-08-02-08
22. Supekar AD, Wakode M. Multi-parameter based mango
grading using image processing and machine learning 33. Bhargava A, Bansal A. Automatic detection and grading of
techniques. NFOCOMP JCS. 2020;19(2):175-187. multiple fruits by machine learning. Food Anal Methods.
2020;13:751-761.
23. Worasawate D, Sakunasinha P, Chiangga S. Automatic
classification of the ripeness stage of mango fruit using a doi: 10.1007/s12161-019-01690-6
machine learning approach. AgriEngineering. 2022;4(1):32-47.
34. Ismail N, Malik OA. Real-time visual inspection system
doi: 10.3390/agriengineering4010003 for grading fruits using computer vision and deep learning
24. Doan TN, Le-Thi DN. A Novel mango grading system techniques. Inf Process Agric. 2022;9(1):24-37.
based on image processing and machine learning methods. doi: 10.1016/j.inpa.2021.01.005
IJACSA. 2023;14(5):1118-1129.
35. Chakraborty SK, Subeesh A, Dubey K, et al. Development
doi: 10.14569/IJACSA.2023.01405115 of an optimally designed real-time automatic citrus fruit
25. Tripathi MK, Neelakantappa M, Mahalle PN, grading-sorting machine leveraging computer vision-
Channapattana SV, Deshmukh G, Prashant G. Detection based adaptive deep learning model. Eng Appl Artif Intell.
and classification of mango fruit-based on feature extraction 2023;120:105826.
applying optimized hybrid LA-FF algorithms. In: Data-Centric doi: 10.1016/j.engappai.2023.105826
Artificial Intelligence for Multidisciplinary Applications. Boca 36. Raza SM, Raza A, Babeker MIA, Haq ZU, Islam MA,
Raton, FL, USA: Chapman and Hall/CRC; 2024. p. 177-185.
Li S. Efficient citrus fruit image classification via a hybrid
doi: 10.1201/9781003461500 hierarchical CNN and transfer learning framework. J Food
26. Kiran PR, Aradwad P, Arunkumar TV, Parray RA. Meas Charact. 2025;19(1):356-377.
Enhancing mango quality control: A novel approach to doi: 10.1007/s11694-024-02973-1
spongy tissue inspection through image clustering and 37. Huong PT, Hien LT, Son NM, Tuan HC, Nguyen TQ.
machine learning models via X-ray imaging. J. Food Process Enhancing deep convolutional neural network models for
Eng. 2024;47(6):e14664.
orange quality classification using MobileNetV2 and data
doi: 10.1111/jfpe.14664 augmentation techniques. J Algorithms Comput Technol.
27. Azeez TB. An automatic mango quality grading system in 2025;19:1-22.
smart agriculture using novel adaptive feature vector and doi: 10.1177/17483026241309070
ensemble learning. Multimed Tools Appl. 2025;84:1-26.
38. Lord M. Validation of an invariant embedding method
doi: 10.1007/s11042-025-20688-3 for Fredholm integral equations. Appl Math Comput.
28. Siddiquee A, Islam MS, Ud Dowla MY, Rezaul KM, Grout V. 1980;7(1):1-7.
Detection, quantification and classification of ripened tomatoes: doi: 10.1016/0096-3003(80)90030-2
A comparative analysis of image processing and machine
learning. IET Image Processing. 2020;14(11):2442-2456. 39. Nantomah K, Ege I. A lambda analogue of the gamma
function and its properties. Res Math. 2022;30(2):18-29.
doi: 10.1049/iet-ipr.2019.0738
doi: 10.15421/242209
29. Ropelewska E, Piecko J. Discrimination of tomato seeds 40. Coulembier K. Homological kernels of monoidal functors.
belonging to different cultivars using machine learning. Eur
Food Res Technol. 2022;248:685-705. Sel Math New Ser. 2023;29(2):24.
doi: 10.1007/s00029-023-00829-y
doi: 10.1007/s00217-021-03920-w
41. Singh P, Singh N, Singh KK, Singh A. Diagnosing of
30. Bai Y, Mao S, Zhou J, Zhang B. Clustered tomato detection disease using machine learning. In: Machine Learning and
and picking point location using machine learning-aided
image analysis for automatic robotic harvesting. Precis the Internet of Medical Things in Healthcare. United States:
Agric. 2023;24(2):727-743. Academic Press; 2020. p. 89-111.
doi: 10.1016/B978-0-12-821229-5.00003-3
doi: 10.1007/s11119-022-09972-6
42. Obi JC. A comparative study of several classification metrics
31. Vo HT, Mui KC, Thien NN, Tien PP. Automating tomato
ripeness classification and counting with YOLOv9. Int J Adv and their performances on data. WJAETS. 2023;8(1):308-314.
Comput Sci Appl. 2024;15(4):1120-1128. doi: 10.30574/wjaets.2023.8.1.0054

Volume 2 Issue 2 (2025) 89 doi: 10.36922/IJAMD025150011