Optimization of Sorghum Spike Recognition Algorithm and Yield Estimation
In the natural field environment, the high planting density of sorghum and severe occlusion among spikes substantially increases the difficulty of sorghum spike recognition, resulting in frequent false positives and false negatives. The target detection model suitable for this environment requires h...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/7/1526 |
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| Summary: | In the natural field environment, the high planting density of sorghum and severe occlusion among spikes substantially increases the difficulty of sorghum spike recognition, resulting in frequent false positives and false negatives. The target detection model suitable for this environment requires high computational power, and it is difficult to realize real-time detection of sorghum spikes on mobile devices. This study proposes a detection-tracking scheme based on improved YOLOv8s-GOLD-LSKA with optimized DeepSort, aiming to enhance yield estimation accuracy in complex agricultural field scenarios. By integrating the GOLD module’s dual-branch multi-scale feature fusion and the LSKA attention mechanism, a lightweight detection model is developed. The improved DeepSort algorithm enhances tracking robustness in occlusion scenarios by optimizing the confidence threshold filtering (0.46), frame-skipping count, and cascading matching strategy (n = 3, max_age = 40). Combined with the five-point sampling method, the average dry weight of sorghum spikes (0.12 kg) was used to enable rapid yield estimation. The results demonstrate that the improved model achieved a mAP of 85.86% (a 6.63% increase over the original YOLOv8), an F1 score of 81.19%, and a model size reduced to 7.48 MB, with a detection speed of 0.0168 s per frame. The optimized tracking system attained a MOTA of 67.96% and ran at 42 FPS. Image- and video-based yield estimation accuracies reached 89–96% and 75–93%, respectively, with single-frame latency as low as 0.047 s. By optimizing the full detection–tracking–yield pipeline, this solution overcomes challenges in small object missed detections, ID switches under occlusion, and real-time processing in complex scenarios. Its lightweight, high-efficiency design is well suited for deployment on UAVs and mobile terminals, providing robust technical support for intelligent sorghum monitoring and precision agriculture management, and thereby playing a crucial role in driving agricultural digital transformation. |
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| ISSN: | 2073-4395 |