@article {233, title = {Grapevine buds detection and localization in 3D space based on Structure from Motion and 2D image classification}, journal = {Computers in Industry}, volume = {99C }, year = {2018}, month = {04/2018}, pages = {303-312}, chapter = {303}, abstract = {

In viticulture, there are several applications where 3D bud detection and localization in vineyards is a necessary task susceptible to automation: measurement of sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, and bud development stage.\ This paper presents a workflow to achieve quality 3D localizations of grapevine buds based on well-known computer vision and machine learning algorithms when provided with images captured in natural field conditions (i.e., natural sunlight and the addition of no artificial elements), during the winter season and using a mobile phone RGB camera. Our pipeline combines the Oriented FAST and Rotated BRIEF (ORB) for keypoint detection, a Fast Local Descriptor for Dense Matching (DAISY) for describing the keypoint, and the Fast Approximate Nearest Neighbor (FLANN) technique for matching keypoints, with the Structure from Motion multi-view scheme for generating consistent 3D point clouds. Next, it uses a 2D scanning window classifier based on Bag of Features and Support Vectors Machine for classification of 3D points in the cloud. Finally, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for 3D bud localization is applied. Our approach resulted in a maximum precision\ of 1.0\ (i.e., no false detections), a maximum recall\ of 0.45\ (i.e. 45\%\ of the buds detected), and a localization error within the range of 259-554\ pixels (corresponding to approximately 3\ bud diameters, or 1.5cm) when evaluated over the whole range of user-given parameters of workflow components.

}, keywords = {computer vision, Grapevine bud detection, Precision viticulture}, issn = {0166-3615}, doi = {10.1016/j.compind.2018.03.033}, url = {https://www.sciencedirect.com/science/article/pii/S0166361517304815}, author = {Diaz, Carlos Ariel and P{\'e}rez, Diego Sebasti{\'a}n and Miatello, Humberto and Bromberg, Facundo} } @article {210, title = {Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines}, journal = {Computers and Electronics in Agriculture}, volume = {135}, year = {2017}, month = {04/2017}, pages = {81-95}, chapter = {81}, abstract = {

In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods. A common and effective family of visual detection algorithms are the scanning-window type, that slide a (usually) fixed size window along the original image, classifying each resulting windowed-patch as containing or not containing the target object. The simplicity of these algorithms finds its most challenging aspect in the classification stage. Interested in grapevine buds detection in natural field conditions, this paper presents a classification method for images of grapevine buds ranging 100 to 1600 pixels in diameter, captured in outdoor, under natural field conditions, in winter (i.e., no grape bunches, very few leaves, and dormant buds), without artificial background, and with minimum equipment requirements. The proposed method uses well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier. When evaluated over images containing buds of at least 100 pixels in diameter, the approach achieves a recall higher than 0.9 and a precision of 0.86 over all windowed-patches covering the whole bud and down to 60\% of it, and scaled up to window patches containing a proportion of 20\%-80\% of bud versus background pixels. This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.

}, keywords = {computer vision, Grapevine bud, Image classification, Precision viticulture, Scanning-window detection}, issn = {0168-1699}, doi = {10.1016/j.compag.2017.01.020}, url = {http://www.sciencedirect.com/science/article/pii/S0168169916301818}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Diaz, Carlos Ariel} } @article {209, title = {Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions}, year = {2016}, institution = {arXiv.org}, abstract = {

Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of the potential production of the plants. This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques. Several methods based on Computer Vision and other techniques are introduced in the literature. These methods present different advantages for crop management: they are amenable to be operated by unknowledgeable personnel, with lower operational costs; they result in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; and they make the measurement process amenable to be embedded in larger autonomous systems, allowing more measurements to be taken with equivalent costs. To date, all existing autonomous methods are either of low precision, or have a prohibitive cost for massive agricultural adoption, leaving the manual Vernier caliper or tape measure as the only choice in most situations. In this work we present a semi-autonomous measurement method that is susceptible to be fully automated, cost effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper manual method.

}, keywords = {computer vision, Gaussian mixture model, Grapevine, Measurements, Trunk Diameter}, issn = {arXiv:1406.4845v2}, url = {http://arxiv.org/abs/1406.4845}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Gonzalez-Antivilo, Francisco} }