SUPER-RESOLUTION RECONSTRUCTION OF UAV IMAGES FOR MAIZE TASSEL DETECTION

The Unmanned Aerial Vehicle (UAV) technology provides essential technical support for maize (Zea mays) cultivation in smart agriculture. To address the challenge of low resolution in UAV maize images due to factors such as flight altitude and instrument parameters, which impact the accurate monitoring and assessment of maize crop growth and further tassel detection tasks, this study proposes an improved enhanced super-resolution generative adversarial network for super-resolution reconstruction of UAV maize images. Firstly, Iterative Attention Feature Fusion is introduced in the generation network, effectively integrating the features of maize and restoring high-frequency details in maize images. Subsequently, the Iterative Attention Feature Fusion is optimized by removing batch normalization and replacing the ReLU activation function with the PReLU activation function, thereby enhancing the model's representation capability. Furthermore, to address the complex features and structures of tassels in maize images, the VGG discriminator network is replaced with a Unet network, thereby improving the model's ability to handle image details and textures. Finally, the model was validated and analyzed on different maize image datasets. Experimental results show that compared with other mainstream super-resolution models, the reconstructed images of our model proposed exhibit higher quality and clarity; for maize tassel detection, the average accuracy of tassel detection in the images reconstructed by our model compared with the low-resolution images was increased by 8.77%similar to 25.71%. This provides a more accurate and clear UAV monitoring method for maize agricultural production and provides more reliable image data support for related decisions and applications.