A two-stage deep learning framework for oil palm fresh fruit bunch ripeness classification toward predictive harvesting in precision agriculture
Open Access
- 5 August 2026
- journal article
- research article
- Published by Springer Nature in Scientific Reports
- Vol. 16 (1)
Abstract
No abstract availableKeywords
This publication has 29 references indexed in Scilit:
- Integrative approach for capsicum maturity detection: from laboratory analysis to machine learning assisted deep learning custom model developmentNeural Computing and Applications, 2026
- Improved YOLOv8 for multi-colored apple fruit instance segmentation and 3D localizationArtificial Intelligence in Agriculture, 2026
- Comprehensive performance evaluation of YOLOv12, YOLO11, YOLOv10, YOLOv9 and YOLOv8 on detecting and counting fruitlet in complex orchard environmentsAgriculture Communications, 2026
- Generative AI in agriculture 4.0: Applications, challenges, and integration in the Indian contextFood and Humanity, 2025
- Fresh Fruit Bunch Ripeness Classification Methods: A ReviewFood and Bioprocess Technology, 2024
- Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identificationComputers and Electronics in Agriculture, 2024
- Shortening harvest interval, reaping benefits? A study on harvest practices in oil palm smallholder farming systems in IndonesiaAgricultural Systems, 2023
- Tomato plant disease detection using transfer learning with C-GAN synthetic imagesComputers and Electronics in Agriculture, 2021
- Instance segmentation of apple flowers using the improved mask R–CNN modelBiosystems Engineering, 2020
- Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’Precision Agriculture, 2019