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This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images. The estimation models for the three targeted features, specifically for each variety, were established by linearly correlating the information extracted from the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains. [ABSTRACT FROM AUTHOR]

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. [ABSTRACT FROM AUTHOR]

Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot's plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards. [ABSTRACT FROM AUTHOR]

• Accurate yield estimation increases earnings and stabilises market price. • Traditional estimations are becoming inaccurate as climate variability increases. • First image dataset (OLIVEnet) to develop solutions for olive-fruit identification. • First algorithm able to identify olive fruits in tree images taken in the field. • Accuracy of results supports viability of yield estimation using image analysis. Accurate yield estimation is a greatly desired objective in oliviculture due to the high economic value of its production. This paper presents a methodology aimed at achieving that end. It comprises an artificial-vision algorithm able to detect visible olives in digital images of olive trees captured directly in the field, at night-time and with artificial illumination. These images were taken in an intensive olive orchard of the Picual Olea europaea L. variety in September 2018 (two months prior to harvesting). Regarding the methodology, first, the images are pre-processed to generate a set of sub-images with high probability of containing an olive, thus reducing the search space by a magnitude of 103. Next, these sub-images are classified by a convolutional neural network (CNN) as olive , if they are centred in an olive fruit, or as other in any other case (even if they contain peripheral fruits). To train and validate the CNN, a special database called OLIVEnet was compiled with two disjoint sets integrating these sub-images. A training and a validation set was built with 234,168 and 299,946 olive and other sub-images, respectively. Five different CNN topologies were tested, correctly classifying the best performing one in 83.13% of olive instances, with a precision of 84.80%, and 99.12% of other instances; measured accuracy and F 1 Score were 0.9822 and 0.8396, respectively. As far as the authors' knowledge goes, this article presents the first image analysis approach to automatically identify olive fruits in an image of the entire tree directly taken in the field. The obtained results constitute a first and solid step towards the implementation of an automatic system for yield estimation of olive orchards. [ABSTRACT FROM AUTHOR]