Detection and counting of wheat ear using YOLOv8

Abstract

Detection and calculation of wheat ears are critical for land management, yield estimation, and crop phenotype analysis. Most methods are based on superficial and color features extracted using machine learning. However, these methods cannot fulfill wheat ear detection and counting in the field due to the limitations of the generated features and their lack of robustness. Various detectors have been created to deal with this problem, but their accuracy and calculation precision still need to be improved. This research proposes a deep learning method using you only look once (YOLO), especially the YOLOv8 model with depth and channel width configuration, stochastic gradient descent (SGD) optimizer, structure modification, and convolution module along with hyperparameter tuning by transfer learning method. The results show that the model achieves a mean average precision (mAP) of 95.80%, precision of 99.90%, recall of 99.50%, and frame per second (FPS) of 22.08. The calculation performance of the wheat ear object achieved accurate performance with a coefficient of determination (R^2) value of 0.977, root mean square error (RMSE) of 2.765, and bias of 1.75.

Code and data availability

The paper trains a modified YOLOv8 on the public GWHD 2020 wheat ear dataset, but that dataset is cited prior work [23], not a paper-specific asset. No author code, trained weights, or data deposit with a public URL is mentioned anywhere in the supplied blocks.

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