@article {aGene-Molad, title = {AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation}, journal = {Data in Brief}, volume = {52}, year = {2024}, month = {02/2024}, abstract = {

The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub- set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research papers titled "Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation" [1] and {\textquotedblleft}Simultaneous fruit detection and size estimation using multitask deep neural networks{\textquotedblright} [2].

}, keywords = {Agricultural robotics, amodal segmentation, depth image, Fruit measurement, Fruit visibility, Instance Segmentation, modal segmentation, Yield prediction}, doi = {https://doi.org/10.1016/j.dib.2023.110000}, author = {Gen{\'e}-Mola, Jordi and Ferrer-Ferrer, M. and Hemming, J. and Dalfsen, P. and Hoog, D. and Sanz-Cortiella, R. and Rosell-Polo, Joan R. and Morros, J.R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @article {aGene-Mola23, title = {Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation}, journal = {Computers and Electronics in Agriculture}, volume = {209}, year = {2023}, month = {04/2023}, abstract = {

The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5\ mm and R2\ =\ 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE\ =\ 2.93\ mm and R2\ =\ 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60\%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at\ https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.

}, keywords = {deep learning, Fruit detection, Fruit measurement, Fruit visibility, Precision agriculture, Yield estimation}, issn = {ISSN 0168-1699}, doi = {https://doi.org/10.1016/j.compag.2023.107854}, url = {https://authors.elsevier.com/sd/article/S0168-1699(23)00242-9}, author = {Gen{\'e}-Mola, Jordi and Ferrer-Ferrer, M. and Gregorio, Eduard and Blok, P. M. and Hemming, J. and Morros, J.R. and Rosell-Polo, Joan R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J.} }