Thiago T. Santos

Amy Tabb, Henry Medeiros, Mitchell J. Feldmann et al.

This paper describes CALICO, a method for multi-camera calibration suitable for challenging contexts: stationary and mobile multi-camera systems, cameras without overlapping fields of view, and non-synchronized cameras. Recent approaches are roughly divided into infrastructure- and pattern-based. Infrastructure-based approaches use the scene's features to calibrate, while pattern-based approaches use calibration patterns. Infrastructure-based approaches are not suitable for stationary camera systems, and pattern-based approaches may constrain camera placement because shared fields of view or extremely large patterns are required. CALICO is a pattern-based approach, where the multi-calibration problem is formulated using rigidity constraints between patterns and cameras. We use a {\it pattern rig}: several patterns rigidly attached to each other or some structure. We express the calibration problem as that of algebraic and reprojection error minimization problems. Simulated and real experiments demonstrate the method in a variety of settings. CALICO compared favorably to Kalibr. Mean reconstruction accuracy error was $\le 0.71$ mm for real camera rigs, and $\le 1.11$ for simulated camera rigs. Code and data releases are available at \cite{tabb_amy_2019_3520866} and \url{https://github.com/amy-tabb/calico}.

Thiago T. Santos, Kleber X. S. de Souza, João Camargo Neto et al.

Traditionally, sweet orange crop forecasting has involved manually counting fruits from numerous trees, which is a labor-intensive process. Automatic systems for fruit counting, based on proximal imaging, computer vision, and machine learning, have been considered a promising alternative or complement to manual counting. These systems require data association components that prevent multiple counting of the same fruit observed in different images. However, there is a lack of work evaluating the accuracy of multiple fruit counting, especially considering (i) occluded and re-entering green fruits on leafy trees, and (ii) counting ground-truth data measured in the crop field. We propose a non-invasive alternative that utilizes fruit counting from videos, implemented as a pipeline. Firstly, we employ CNNs for the detection of visible fruits. Inter-frame association techniques are then applied to track the fruits across frames. To handle occluded and re-appeared fruit, we introduce a relocalization component that employs 3-D estimation of fruit locations. Finally, a neural network regressor is utilized to estimate the total number of fruit, integrating image-based fruit counting with other tree data such as crop variety and tree size. The results demonstrate that the performance of our approach is closely tied to the quality of the field-collected videos. By ensuring that at least 30% of the fruit is accurately detected, tracked, and counted, our yield regressor achieves an impressive coefficient of determination of 0.85. To the best of our knowledge, this study represents one of the few endeavors in fruit estimation that incorporates manual fruit counting as a reference point for evaluation. We also introduce annotated datasets for multiple orange tracking (MOrangeT) and detection (OranDet), publicly available to foster the development of novel methods for image-based fruit counting.

Thiago T. Santos, Luciano Gebler

Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available.

Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos et al.

Agricultural applications such as yield prediction, precision agriculture and automated harvesting need systems able to infer the crop state from low-cost sensing devices. Proximal sensing using affordable cameras combined with computer vision has seen a promising alternative, strengthened after the advent of convolutional neural networks (CNNs) as an alternative for challenging pattern recognition problems in natural images. Considering fruit growing monitoring and automation, a fundamental problem is the detection, segmentation and counting of individual fruits in orchards. Here we show that for wine grapes, a crop presenting large variability in shape, color, size and compactness, grape clusters can be successfully detected, segmented and tracked using state-of-the-art CNNs. In a test set containing 408 grape clusters from images taken on a trellis-system based vineyard, we have reached an F 1 -score up to 0.91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape. We have also shown as clusters can be identified and tracked along video sequences recording orchard rows. We also present a public dataset containing grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images. The presented pipeline for annotation, training, evaluation and tracking of agricultural patterns in images can be replicated for different crops and production systems. It can be employed in the development of sensing components for several agricultural and environmental applications.