An End-to-End Deep Learning Framework for Crop Type and Phenological Stage Identification
Year: 2026
Type: article
Abstract: In precision agriculture, crop monitoring is imperative for determining optimal harvest periods. Detecting early growth stages is non-trivial for crops with small seeds, as standard image resizing can remove crucial pixel-level details. Furthermore, an insufficiency of data in precision agriculture limits the effectiveness of AI tools. To address this data limitation, a new labeled dataset was introduced to the literature comprising four growth stages—seed, bud, flower, and harvestable fruit—of okra (Abelmoschus esculentus), pepper (Capsicum annuum), and eggplant (Solanum melongena). The images for the growth stages were collected from agricultural fields in Mugla, Türkiye, and supplemented with diverse open-source data to enhance model generalization. To organize plant growth stage detection within a single pipeline, we introduced a comprehensive framework comprising data curation, preprocessing, and deep learning architectures, including YOLO variants, Faster R-CNN, and RT-DETR-L. Dynamic Canny edge-density maps were also integrated into the deep learning models to preserve the representation of small seeds. To address the severe class imbalance in the dataset, a custom Class-Balanced Focal Loss mechanism was integrated. To ensure the validity and generalization of the developed models, a five-step ablation study and cross-distribution validation were carried out. The results showed that the developed YOLO framework can accurately achieve the 12-class prediction of crop stages, with a superior localization precision consisting of a [email protected]:0.95 of 72.81% and a peak mean IoU of 0.8876 for true positives. Ultimately, the primary contribution of this work is a highly valuable labeled dataset, supported by a robust detection pipeline for future plant phenology identification tasks.
Source: IEEE Access
Institution: Bursa Uludağ Üni̇versi̇tesi̇
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W7165797784