Towards automated crop monitoring: A computer vision solution for maize tassel detection under center-pivot irrigation systems

Abstract

The center-pivot irrigation system is a highly efficient water-saving agricultural system. However, it typically operates solely based on predefined paths, speeds, and water volumes, and cannot assess the true needs of the crops due to its inability to monitor their growth status. To address this limitation, we deployed cameras on the center-pivot irrigation system to establish a mobile phenotyping platform, and developed a maize tassel detection model based on the YOLOv11 architecture. Several key improvements were implemented to address the complex morphology and challenging feature extraction of maize tassels: The ODConv (Omni-Dimensional Convolution) module was introduced to more comprehensively capture dynamic tassel features through a dynamic convolution strategy; To suppress interference from complex field environments on detection results, the SE (Squeeze-and-Excitation) attention mechanism was embedded to enhance effective feature responses and reduce noise impact; The BiFPN (Weighted Bi-directional Feature Pyramid Network) structure was adopted to strengthen multi-scale feature fusion capabilities, further improving the model’s detection performance for tassels at various scales; To tackle the difficulty of tassel recognition and localization in field environments, the C3K2 module was fused with the CSAM (Cross-Slice Attention Mechanism) to construct a C3K2-CSAM module, achieving more precise tassel identification and localization. Experimental results demonstrate that the OSBC-YOLO model achieved a precision of 91.8 % and a mean average precision (mAP) of 85.7 %, while reducing the number of parameters, FLOPs, and memory usage by 8.1 %, 28.1 %, and 7.3 % respectively compared to the original model. Field tests conducted on the center-pivot irrigation system revealed an error rate of 5.87 % between the number of tassels detected by the model and the ground truth count. These results verify that the OSBC-YOLO model can effectively perform tassel counting in practical operating environments and possesses the capability for recognition of crop phenotypic traits. This system provides critical data support for intelligent variable-rate irrigation decisions, promoting the transformation of center-pivot irrigation systems from traditional “single-function operation” to an integrated “perception-decision-execution” mode.

Plant phenotyping relevance

トウモロコシ雄穂の検出・計数を行う移動型画像表現型解析プラットフォームとモデルを開発・検証しており、植物形態形質の取得が中心的貢献である。

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