Accurate fruit size estimation is crucial for plant phenotyping,as it enables precise crop management and en-hances agricultural productivity by providing essential data for growth and resource efficiency analysis.In ...Accurate fruit size estimation is crucial for plant phenotyping,as it enables precise crop management and en-hances agricultural productivity by providing essential data for growth and resource efficiency analysis.In this study,we estimated the size of on-plant oriental melons grown in a vertical cultivation system to address the challenges posed by leaf occlusion.Data augmentation was achieved using a diffusion model to generate syn-thetic leaves to cover existing fruits and create an enriched dataset.Three instance segmentation models-mask region-based convolutional neural network(CNN),Mask2Former,and detection transformer(DETR)-and six de-occlusion models derived from these architectures were implemented.These models successfully inferred both visible and occluded areas of the fruit.Notably,Amodal Mask2Former and occlusion-aware RCNN(ORCNN)achieved average precision scores of 85.92%and 85.35%,respectively.The inferred masks were used to es-timate the height and diameter of the fruit,with Amodal Mask2Former yielding a mean absolute error of 5.46 mm and 4.20 mm and a mean absolute percentage error of 4.86%and 5.33%,respectively.The results indicate enhanced performance of the transformer-based Amodal Mask2Former over CNN architectures in de-occlusion tasks and size estimation.Finally,the enhancement in de-occlusion models compared to conventional models was assessed and demonstrated across occlusion ratios ranging from 0 to 70%.However,generating synthetic datasets with occlusion ratios over 70%remains a limitation.展开更多
基金This work was supported by the Rural Development Administration(RDA)through the Cooperative Research Program for Agriculture Science and Technology Development[Project No.RS-2024-00440583].
文摘Accurate fruit size estimation is crucial for plant phenotyping,as it enables precise crop management and en-hances agricultural productivity by providing essential data for growth and resource efficiency analysis.In this study,we estimated the size of on-plant oriental melons grown in a vertical cultivation system to address the challenges posed by leaf occlusion.Data augmentation was achieved using a diffusion model to generate syn-thetic leaves to cover existing fruits and create an enriched dataset.Three instance segmentation models-mask region-based convolutional neural network(CNN),Mask2Former,and detection transformer(DETR)-and six de-occlusion models derived from these architectures were implemented.These models successfully inferred both visible and occluded areas of the fruit.Notably,Amodal Mask2Former and occlusion-aware RCNN(ORCNN)achieved average precision scores of 85.92%and 85.35%,respectively.The inferred masks were used to es-timate the height and diameter of the fruit,with Amodal Mask2Former yielding a mean absolute error of 5.46 mm and 4.20 mm and a mean absolute percentage error of 4.86%and 5.33%,respectively.The results indicate enhanced performance of the transformer-based Amodal Mask2Former over CNN architectures in de-occlusion tasks and size estimation.Finally,the enhancement in de-occlusion models compared to conventional models was assessed and demonstrated across occlusion ratios ranging from 0 to 70%.However,generating synthetic datasets with occlusion ratios over 70%remains a limitation.
