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To address poor accuracy in identifying open-field vegetables when light is blocked, this investigation used cabbage as the subject. It established an online target identification model based on deep learning. Using YOLOv8n as the backbone network, an approach was formulated to reduce the impact of light occlusion on the precision of online cabbage identification. A sequence of cabbage image filters was engineered to remove the consequences of light occlusion. A unit for adaptive parameter acquisition tailored to the cabbage image filters was built. The image filter sequence and the adaptive acquisition unit were woven into the YOLOv8n object detection architecture. This fusion enabled precise, real-time identification of cabbage in light-occluded environments. Experimental outcomes indicated identification precisions of 97.5% with the normal lighting dataset, 93.1% with the light occlusion dataset, and 95.0% with the mixed dataset. When the degree of light occlusion exceeded 0.4, the identification precision increased by 9.9% and 13.7% compared with the YOLOv5n and YOLOv8n architectures. The model achieved identification precisions of 99.3% with the Chinese cabbage dataset and 98.3% with the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge device, where it processed images at 26.32 frames per second. Field assessments showed identification precisions of 96.0% in ordinary light and 91.2% when light was blocked. The presented online cabbage identification model facilitates instant recognition and spatial location of cabbage in complex open-field conditions, providing technical support for targeted spraying.

Precise forecasting of watermelon harvest volumes is vital within the agricultural supply chain, since it informs the deployment of farming inputs and streamlines stock and logistical scheduling. Conventional harvest assessment depends predominantly on human effort, rendering it both labour-intensive and slow. To tackle this issue, the present study proposes an algorithmic workflow that leverages unmanned aerial vehicle (UAV) video to identify and count watermelons. The workflow combines You Only Look Once version 8s (YOLOv8s) with image mosaicking and overlapping region division, which supports whole-field watermelon quantity estimation. The detection model, built on YOLOv8s and refined via transfer learning, achieved 99.20% detection accuracy, underscoring its suitability for crop forecasting. The counting strategy, which employs image mosaicking and overlapping region division, accepts stitched panoramas as input and significantly reduces double counting compared to the video-tracking-based detection and counting method. Counting accuracy exceeded 96.61%, indicating strong promise for yield forecasting applications. This elevated accuracy attests to the practicability of deploying the proposed approach for total harvest estimation across expansive watermelon cultivations.