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opencc-by-4.0Mar 2020View details →

Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions

This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain  under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO2 emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data. These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].

opencc-by-4.0Nov 2021View details →

FlexiGroBots - Blueberry orchard UAV dataset

FlexiGroBots - Blueberry orchard UAV dataset Acquisition date: 02.07.2021. Location: Babe, Serbia Dataset consists of UAV drone images: - 6 channels: reflectance blue, reflectance green, reflectance red, reflectance red edge, reflectance NIR, RGB. In order to align different channels, registration was applied and because of uneven illumination during the acquisition process, illumination correction was performed. Thus, the results of each preprocessing step are located in separate folders, i.e. raw data are in 100FPLAN and 101FPLAN, results of registration are in 100FPLAN_registrated and 101FPLAN_registrated, while images with corrected illumination are in 100FPLAN_registrated_corrected and 101FPLAN_registrated_corrected. Orthomosaics created before and after preprocessing are located in UAV orthomosaics folder.

opencc-by-4.0Oct 2022View details →

Olive orchard stress assessment with supplementary Sentinel-2 data

This file contains ground truthing data from olive orchards in Halkidiki, N.Greece and Sentinel-2 data for the noted samples. The samples collected for ground truthing are polygons inside the borders of olive orchards in Halkidiki. Polygons or Sampling units contain information associated with biotic and abiotic stress-related assessments carried out by visual inspection and laboratory analysis of samples with ongoing symptoms. Assessments were recorded as percentages of symptoms observed in the total vegetation surface present in each sampling unit. Symptom percentages were attributed to three possible classes: Verticillium dahliae, Spilocaea oleaginea, Unidentified Stress Factors. These percentage values were used to characterize healthy trees and the incidence of V. dahliae, S. oleaginea and unidentified stress factors (USF) in the sample. USF was used for all other non-classified surveyed symptoms attributed to diseases, pests, or abiotic-related damage. Percentages were summed to compute the total stress present in each sampling unit. “Total stress” refers to the stress incidence value used together with different thresholds to create binary labels for each sample of “stressed” or “not stressed”. The polygon geographical information for each sample were recorded and stored in shapefile format using SW maps, a mobile mapping and GIS app. Sentinel-2 data was paired with each sample using the Feature Info Service (FIS) available from sentinel hub, now upgraded into the Statistical API tool ( & ). This API enables acquisition of statistics calculated based on satellite imagery without having to download images. In the Statistical API request the area of interest, time period, evalscript and statistical measures of interest can be calculated. The requested statistics are returned in the API response. Statistical API deployments: <a href="https://creodias.sentinel-hub.com/api/v1/statistics">https://creodias.sentinel-hub.com/api/v1/statistics</a> <a href="https://services.sentinel-hub.com/api/v1/statistics">https://services.sentinel-hub.com/api/v1/statistics</a> <a href="https://services-uswest2.sentinel-hub.com/api/v1/statistics">https://services-uswest2.sentinel-hub.com/api/v1/statistics</a>

opencc-by-4.0Mar 2023View details →

FlexiGroBots - Blueberry orchard UAV dataset August 2022 - raw data

This data represents the UAV image acquisition from August 2022 in blueberry orchards located in Babe, Serbia. This is the first part of larger dataset, that contains raw images and orthomosaics generated using these images. Raw images are in 100FPLAN, 101FPLAN, and 102PLAN, while generated orthomosaics are in folder Raw orthomosaics August. Another dataset will be uploaded with preprocessed data titled in the similar manner.

opencc-by-4.0Apr 2023View details →

FlexiGroBots Ground-level Blueberry Orchard Dataset v1 - RGB Bush Detection Dataset

Ground-level Blueberry Orchard Dataset v1 consists of 2000 RGB images of blueberry orchard scenes captured in the village of Babe, Serbia on three occasions in March, May, and August of 2022. Images are captured using the RGB module of Luxonis OAK-D device, with the resolution of 1920×1080 pixels and stored in the lossless PNG format.  The dataset is created for the purpose of training deep learning models for blueberry bush detection, for the task of autonomous UGV guidance. It contains sequences of images captured from the UGV moving and rotating in blueberry orchard rows. Images are captured from a height of approximately 0.5 meters, with the camera angled towards the base of a blueberry plant and the surrounding bank on which it grows. Dataset is captured in real-life outdoor conditions and contains multiple sources of variability (bush shape and size, lighting conditions, shadows, saturation etc.) and artifacts (occlusions by weeds, branches, presence of irregular objects etc.). There are two classes of annotated objects of interest: <ul> <li> Bush, corresponding to the base of the blueberry bush. </li> <li> Pole, corresponding to hail netting poles and similar obstructing objects such as lamp posts or wooden legs of bumblebee hives (distinguishing poles is important to prevent equipment damage in operations such as soil sampling and pruning). </li> </ul> Objects of interest are annotated with bounding boxes. Labels are saved in two formats: <ul> <li> LabelMe JSON format (x1, y1, x2, y2; in pixels) </li> <li> Yolo TXT format (x_center, y_center, width, height; as a ratio of total image size, with numerical labels 0 and 1 corresponding to Bush and Pole) </li> </ul> There are 61 images with no annotated objects, and there are no corresponding label files for these images. The dataset is split into train, validation and test sets with 75%, 10%, and 15% split (1490, 200, and 310 images, respectively). As the data contains sequences of images, the split is made based on sequences rather than individual images to prevent data leakage. Detailed description and statistics are available in: V. Filipović, D. Stefanović, N. Pajević, Ž. Grbović, N. Đurić and M. Panić, "Bush Detection for Vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, Canada, 2023. (Accepted)

opencc-by-4.0Jan 2021View details →

Data: Flower visiting insects of kiwifruit within New Zealand commercial orchard blocks sampled over two years in the Bay of Plenty, New Zealand

These data are total counts of individual bee and non–bee insects observed visiting the flowers of kiwifruit (Actinidia chinensis var.deliciosa) (‘Hayward’) vines in three commercial orchards located in the Bay of Plenty Region of New Zealand (37° 46' 56" S; 176° 19' 10" E). Each block was located on a different farm and each separated by a distance of at least two kilometres and surveyed twice in two consecutive years. A total of 1181 insects were observed, 741 in the 2014 season and 460 in the 2015 season. Insects from four orders were recorded. The most abundant species were honey bees Apis mellifera (n= 1068; 90.4%), flower longhorn beetles Zorion guttigerum (n= 52; 4.4%), the native bee Lasioglossum sordidum/cognatum (n= 12; 1.0%) and the hover fly Melanostoma fasciatum (n= 11; 0.9%)  Others insects represented 3.2% of individuals observed (n=38). We present a table of counts of the insects observed.

Maintaining habitat diversity at small scales benefits wild bees and pollination services in mountain apple orchards

In 2021, we conducted our study in apple orchards in South Tyrol, an Alpine region in Italy, using pan-traps, direct observations of visitation frequency, and a pollinator exclusion experiment. We investigated the scale-dependent effects of landscape heterogeneity and other parameters on wild bee assemblages and the related pollination service they provide at five spatial scales (radius 100 – 2,000 m).

Figure 7 in Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye

Figure 7: Plant parasitic nematode c-p classification from five fruit orchards in Osmaneli, Bilecik, Türkiye.

Figure 3 in Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye

Figure 3: Free-living nematode c-p classification from five fruit orchards in Osmaneli, Bilecik, Türkiye.

Figure 2 in Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye

Figure 2: Comparative maturity index analysis of nematode c-p classification from five fruit orchards in Osmaneli, Bilecik, Türkiye.

Figure 1 in Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye

Figure 1: Sampling sites, fruit tree orchards: cherry (a), nectarine (b), olive (c), plum (d), walnut (e), peach (f) trees.

Figure 4 in Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye

Figure 4: Food web analysis (Enrichment/Structure indices) from five fruit orchards in Osmaneli, Bilecik, Türkiye.

Figure 6 in Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye

Figure 6: Distribution (%) of feeding types within the plant-parasitic nematodes from Osmaneli, Bilecik, Türkiye.

Fig. 3 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards

Fig. 3. Mean cumulative number by treatment of Stenoma catenifer (IC95) caught in traps baited with synthetic sex pheromones at different trap densities in Hass avocado orchards, Colima, Mexico, during the experiment in 2018. Means with the same lowercase letter are not significantly different from each other according to Tukey's test (X0.05). 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.

Fig. 5 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards

Fig. 5. Relationship between total number of Stenoma catenifer caught in different treatments in 4 Hass avocado orchards in Colima, Mexico, 2018. 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.

Fig. 2 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards

Fig. 2. Number of Stenoma catenifer caught in synthetic sex pheromone traps placed at different densities (1 T2h, 1Th, 2Th, and 3Th: treatments, number of traps per area) and in different Hass avocado orchards (1–4 of the Y right axis) in the municipalities of Comala and Cuauhtémoc, Colima, Mexico, 2018. The columns correspond to treatments and the rows to experimental orchards. 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.

Fig. 4 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards

Fig. 4. Nonparametric bootstrap sampling distribution of the total numbers of Stenoma catenifer caught in experimental plots (CI95%) (1–4) in the linear model of the different orchards. The black dot on each line indicates the mean value of the total for each of the treatments. 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.

Figure 3 in Spatial distribution of Ceratitis capitata in guava orchards and influences from orchard management

Figure 3. Estimates of the parameters of the regression analysis by the Taylor power model, adjusted by the F test to evaluate the spatial distribution and the t-test to compare the hypotheses of the aggregation index of Ceratitis capitata in guava orchards, Ivinhema-MS. Tests: ANOVA (F = 304.05; p <0.001; g.l = 20); t (5.17; p <0.001) for the Alpha hypothesis (h0: a = 1 vs h1 a ≠ 1, where: Alpha (a <0, a = 0 and a>0) and; t (17.44; p <0.001) for the Beta hypothesis (h0: b = 1 vs h1: b ≠ 1), where: Beta (b <1, b = 1 and b> 1).

Figure 2 in Spatial distribution of Ceratitis capitata in guava orchards and influences from orchard management

Figure 2. Number of fruit fly/trap/day (FTD) of Ceratitis capitata and range of negative binomial thresholds (Bn) with other distributions, establishing the levels of safety and control activities for the Mediterranean fly in three guava orchards, Ivinhema, MS, Brazil.