AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation.

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 15,335 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 paper titled "Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation" [1].

PFuji-Size dataset: A collection of images and photogrammetry-derived 3D point clouds with ground truth annotations for Fuji apple detection and size estimation in field conditions.

The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees (Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of 6 complete Fuji apple trees containing a total of 615 apples. The resulting point clouds were 3D segmented by identifying the 3D points corresponding to each apple (3D instance segmentation), obtaining a single point cloud for each apple. All segmented apples were labelled with ground truth diameter annotations. Since the data was acquired in field conditions and at different maturity stages, the set includes different fruit diameters -from 26.9 mm to 94.8 mm- and different fruit occlusion percentages due to foliage. In addition, 25 apples were photographed 360° in laboratory conditions, obtaining high resolution 3D point clouds of this sub-set. To the best of the authors' knowledge, this is the first publicly available dataset for apple size estimation in field conditions. This dataset was used to evaluate different fruit size estimation methods in the research article titled "In-field apple size estimation using photogrammetry-derived 3D point clouds: comparison of 4 different methods considering fruit occlusion" (Gené-Mola et al., 2021).

Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions.

The use of 3D sensors combined with appropriate data processing and analysis has provided tools to optimise agricultural management through the application of precision agriculture. The recent development of low-cost RGB-Depth cameras has presented an opportunity to introduce 3D sensors into the agricultural community. However, due to the sensitivity of these sensors to highly illuminated environments, it is necessary to know under which conditions RGB-D sensors are capable of operating. This work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features. The methodology was applied to evaluate the performance of the Microsoft Kinect v2 sensor in an apple orchard. The results show that sensor resolution and precision decreased significantly under middle to high ambient illuminance (>2000 lx). However, this effect was minimised when measurements were conducted closer to the target. In contrast, illuminance levels below 50 lx affected the quality of colour data and may require the use of artificial lighting. The methodology was useful for characterizing sensor performance throughout the full range of ambient conditions in commercial orchards. Although Kinect v2 was originally developed for indoor conditions, it performed well under a range of outdoor conditions.

Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry.

The present dataset contains colour images acquired in a commercial Fuji apple orchard (Malus domestica Borkh. cv. Fuji) to reconstruct the 3D model of 11 trees by using structure-from-motion (SfM) photogrammetry. The data provided in this article is related to the research article entitled "Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry" [1]. The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. With that, this is the first dataset for fruit detection containing images acquired in a motion sequence to build the 3D model of the scanned trees with SfM and including the corresponding 2D and 3D apple location annotations. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data.

LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions.

This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an air-assisted sprayer used to generate different air flow conditions. A total of 8 scans per tree were performed, including scans from different LiDAR sensor positions (multi-view approach) and under different air flow conditions. These variability of the scanning conditions allows to use the LFuji-air dataset not only for training and testing new fruit detection algorithms, but also to study the usefulness of the multi-view approach and the application of forced air flow to reduce the number of fruit occlusions. The data provided in this article is related to the research article entitled "Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow" [1].

Determination of spray drift and buffer zones in 3D crops using the ISO standard and new LiDAR methodologies.

Spray drift generated in the application of plant protection products in tree crops (3D crops) is a major source of environmental contamination, with repercussions for human health and the environment. Spray drift contamination acquires greater relevance in the EU Southern Zone due to the crops structure and the weather conditions. Hence, there is a need to evaluate spray drift when treating the most representative 3D crops in this area. For this purpose, 4 spray drift tests, measuring airborne and sedimenting spray drift in accordance with ISO 22866:2005, were carried out for 4 different crops (peach, citrus, apple and grape) in orchards of the EU Southern Zone, using an air-blast sprayer equipped with standard (STN) and spray drift reduction (DRN) nozzle types. A further 3 tests were carried out to test a new methodology for the evaluation of spray drift in real field conditions using a LiDAR system, in which the spray drift generated by different sprayer and nozzle types was contrasted. The airborne spray drift potential reduction (DPRV) values, obtained following the ISO 22866:2005, were higher than those for sedimenting spray drift potential reduction (DPRH) (63.82%-94.42% vs. 39.75%-69.28%, respectively). For each crop and nozzle type combination, a sedimenting spray drift model was also developed and used to determine buffer zone width. The highest buffer width reduction (STN vs DRN) was obtained in peach (˃75%), while in grape, citrus and apple only 50% was reached. These results can be used as the starting point to determine buffer zone width in the countries of the EU Southern Zone depending on different environmental threshold values. Tests carried out using LiDAR system demonstrated high capacity and efficiency of this system and this newly defined methodology, allowing sprayer and nozzle types in real field conditions to be differentiated and classified.

Special Issue on "Terrestrial Laser Scanning": Editors' Notes.

In this editorial, we provide an overview of the content of the special issue on "Terrestrial Laser Scanning". The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data.

This article contains data related to the research article entitle "Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities" [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGB-DS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.

Assessment of spray drift potential reduction for hollow-cone nozzles: Part 2. LiDAR technique.

Pesticide spray drift poses health hazards to humans and causes a significant impact on the environment. In this work the capacity of an ad hoc light detection and ranging (LiDAR) system to differentiate spray nozzles according to their potential drift risk is evaluated for the first time. A total of 23 drift potential tests using 10 hollow-cone nozzles were carried out with the sprayer kept in a static position. Drift potential reduction (DPR) values of between 88.6% and 93.6% were obtained when comparing standard and drift reduction nozzle types. It was also possible to order different standard nozzle sizes according to their DPR. The LiDAR signal was correlated with several droplet size parameters measured by a phase Doppler particle analyzer (PDPA), being V100 the best indicator. In the four field tests that were performed, the LiDAR system was also able to differentiate between standard and drift reduction nozzles under real application conditions, obtaining a DPR of 56.7%. The results of this work demonstrate that the developed LiDAR system is an advantageous alternative for the assessment of drift potential reduction.

Assessment of spray drift potential reduction for hollow-cone nozzles: Part 1. Classification using indirect methods.

Spray drift is one of the main pollution sources identified when pesticides are sprayed on crops. In this work, in order to simplify the evaluation of hollow-cone nozzles according to their drift potential reduction, several models commonly used were tested by three indirect methods: phase Doppler particle analyser (PDPA) and two different wind tunnels. The main aim of this study is then to classify for the first time these hollow-cone nozzle models all of them used in tree crop spraying (3D crops). A comparison between these indirect methods to assess their suitability and to provide guidelines for a spray drift classification of hollow-cone nozzles was carried out. The results show that, in general terms, all methods allow hollow-cone nozzle classifications according to their drift potential reduction (DPR) with a similar trend. Among all the parameters determined with the PDPA, the V100 parameter performed best in differentiating the tested nozzles among drift reduction classes. In the wind tunnel, similar values were obtained for both sedimenting and airborne drift depositions. The V100 parameter displayed a high correlation (up to R2 = 0.948) with the drift potential tested with the wind tunnel. It is concluded that in general, the evaluated indirect methods provide equivalent classification results. Additional studies with a greater variety of nozzle types are required to achieve a proposal of harmonized methodology for testing hollow-cone nozzles.

Measurement of Spray Drift with a Specifically Designed Lidar System.

Field measurements of spray drift are usually carried out by passive collectors and tracers. However, these methods are labour- and time-intensive and only provide point- and time-integrated measurements. Unlike these methods, the light detection and ranging (lidar) technique allows real-time measurements, obtaining information with temporal and spatial resolution. Recently, the authors have developed the first eye-safe lidar system specifically designed for spray drift monitoring. This prototype is based on a 1534 nm erbium-doped glass laser and an 80 mm diameter telescope, has scanning capability, and is easily transportable. This paper presents the results of the first experimental campaign carried out with this instrument. High coefficients of determination (R² > 0.85) were observed by comparing lidar measurements of the spray drift with those obtained by horizontal collectors. Furthermore, the lidar system allowed an assessment of the drift reduction potential (DRP) when comparing low-drift nozzles with standard ones, resulting in a DRP of 57% (preliminary result) for the tested nozzles. The lidar system was also used for monitoring the evolution of the spray flux over the canopy and to generate 2-D images of these plumes. The developed instrument is an advantageous alternative to passive collectors and opens the possibility of new methods for field measurement of spray drift.

Eye-safe lidar system for pesticide spray drift measurement.

Spray drift is one of the main sources of pesticide contamination. For this reason, an accurate understanding of this phenomenon is necessary in order to limit its effects. Nowadays, spray drift is usually studied by using in situ collectors which only allow time-integrated sampling of specific points of the pesticide clouds. Previous research has demonstrated that the light detection and ranging (lidar) technique can be an alternative for spray drift monitoring. This technique enables remote measurement of pesticide clouds with high temporal and distance resolution. Despite these advantages, the fact that no lidar instrument suitable for such an application is presently available has appreciably limited its practical use. This work presents the first eye-safe lidar system specifically designed for the monitoring of pesticide clouds. Parameter design of this system is carried out via signal-to-noise ratio simulations. The instrument is based on a 3-mJ pulse-energy erbium-doped glass laser, an 80-mm diameter telescope, an APD optoelectronic receiver and optomechanically adjustable components. In first test measurements, the lidar system has been able to measure a topographic target located over 2 km away. The instrument has also been used in spray drift studies, demonstrating its capability to monitor the temporal and distance evolution of several pesticide clouds emitted by air-assisted sprayers at distances between 50 and 100 m.