A PVT-based wheat detection method

Wheat detection is significant in agriculture and can help farmers achieve efficient planting management and precision agriculture. Traditional wheat detection methods usually rely on hand-designed feature extractors and classifiers, but the ability and generalization of feature representations limit their performance. In contrast, deep learning-based methods can automatically learn feature representations and perform end-to-end training on large-scale datasets, with more vital representation ability and generalization. In this study, we propose a PVT-based detection method for wheat. PVT is a visual model combining a Transformer and feature pyramid, simultaneously capturing global and local information in images. The experimental results show that the PVT model is adaptable and can be generalized in wheat detection. It can provide accurate detection results of wheat ears and maintain stability when dealing with different light conditions and wheat growth cycles.