Multi head attention based deep learning framework for waxberry fruit object segmentation from high resolution remote sensing images

In some Asian countries, waxberries are special fruit that demand substantial labour for harvesting each season. To ease this burden, automated fruit-picking equipment has seen extensive development over the past decade. However, accurately segmenting waxberries in orchards remains challenging due to complex environments with overlapping fruits, foliage occlusions, and variable lighting conditions. Most existing segmentation methods are optimized for controlled environments with steady lighting and unobstructed views of the fruit, which limits their effectiveness in real-world scenarios. This paper introduces a fully convolutional neural network namely Multi-Attention Waxberry Network (MAWNet) which effectively addresses challenges such as occlusions, overlapping fruits and variable lighting conditions. The MAWNet is a UNet based architecture and it consist of enhanced residual block, transformer block, Atrous Spatial Pyramid Pooling (ASPP) block and introduced Multiple Dilation Convolutional (MDC) Block. The experimental results validate that the proposed MAWNet model surpasses several State-of-the-Art (SOTA) architectures, in terms of performance with achieving a remarkable accuracy of 99.63%, an Intersection over Union (IoU) of 96.77%, and a Dice coefficient of 98.34%.