Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard
Precise photogrammetric mapping of preharvest conditions in an apple orchard can help determine the exact position and volume of single apple fruits. This can help estimate upcoming yields and prevent losses through spatially precise cultivation measures. These parameters also are the basis for effe...
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MDPI AG
2025-01-01
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| author | Marius Hobart Michael Pflanz Nikos Tsoulias Cornelia Weltzien Mia Kopetzky Michael Schirrmann |
| author_facet | Marius Hobart Michael Pflanz Nikos Tsoulias Cornelia Weltzien Mia Kopetzky Michael Schirrmann |
| author_sort | Marius Hobart |
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| description | Precise photogrammetric mapping of preharvest conditions in an apple orchard can help determine the exact position and volume of single apple fruits. This can help estimate upcoming yields and prevent losses through spatially precise cultivation measures. These parameters also are the basis for effective storage management decisions, post-harvest. These spatial orchard characteristics can be determined by low-cost drone technology with a consumer grade red-green-blue (RGB) sensor. Flights were conducted in a specified setting to enhance the signal-to-noise ratio of the orchard imagery. Two different altitudes of 7.5 m and 10 m were tested to estimate the optimum performance. A multi-seasonal field campaign was conducted on an apple orchard in Brandenburg, Germany. The test site consisted of an area of 0.5 ha with 1334 trees, including the varieties ‘Gala’ and ‘Jonaprince’. Four rows of trees were tested each season, consisting of 14 blocks with eight trees each. Ripe apples were detected by their color and structure from a photogrammetrically created three-dimensional point cloud with an automatic algorithm. The detection included the position, number, volume and mass of apples for all blocks over the orchard. Results show that the identification of ripe apple fruit is possible in RGB point clouds. Model coefficients of determination ranged from 0.41 for data captured at an altitude of 7.5 m for 2018 to 0.40 and 0.53 for data from a 10 m altitude, for 2018 and 2020, respectively. Model performance was weaker for the last captured tree rows because data coverage was lower. The model underestimated the number of apples per block, which is reasonable, as leaves cover some of the fruits. However, a good relationship to the yield mass per block was found when the estimated apple volume per block was combined with a mean apple density per variety. Overall, coefficients of determination of 0.56 (for the 7.5 m altitude flight) and 0.76 (for the 10 m flights) were achieved. Therefore, we conclude that mapping at an altitude of 10 m performs better than 7.5 m, in the context of low-altitude UAV flights for the estimation of ripe apple parameters directly from 3D RGB dense point clouds. |
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| id | doaj-art-632feff501674fbaa126def15f533357 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
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| series | Drones |
| spelling | doaj-art-632feff501674fbaa126def15f5333572025-01-24T13:29:49ZengMDPI AGDrones2504-446X2025-01-01916010.3390/drones9010060Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple OrchardMarius Hobart0Michael Pflanz1Nikos Tsoulias2Cornelia Weltzien3Mia Kopetzky4Michael Schirrmann5Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, GermanyLandesamt für Umwelt, Seeburger Chaussee 2, 14476 Potsdam, GermanyInstitut für Technik, Geisenheim University, Von-Lade-Straße 1, 65366 Geisenheim, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, GermanySchool of Sustainability, Leuphana-Universität Lüneburg, Universitätsallee 1, 21335 Lüneburg, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, GermanyPrecise photogrammetric mapping of preharvest conditions in an apple orchard can help determine the exact position and volume of single apple fruits. This can help estimate upcoming yields and prevent losses through spatially precise cultivation measures. These parameters also are the basis for effective storage management decisions, post-harvest. These spatial orchard characteristics can be determined by low-cost drone technology with a consumer grade red-green-blue (RGB) sensor. Flights were conducted in a specified setting to enhance the signal-to-noise ratio of the orchard imagery. Two different altitudes of 7.5 m and 10 m were tested to estimate the optimum performance. A multi-seasonal field campaign was conducted on an apple orchard in Brandenburg, Germany. The test site consisted of an area of 0.5 ha with 1334 trees, including the varieties ‘Gala’ and ‘Jonaprince’. Four rows of trees were tested each season, consisting of 14 blocks with eight trees each. Ripe apples were detected by their color and structure from a photogrammetrically created three-dimensional point cloud with an automatic algorithm. The detection included the position, number, volume and mass of apples for all blocks over the orchard. Results show that the identification of ripe apple fruit is possible in RGB point clouds. Model coefficients of determination ranged from 0.41 for data captured at an altitude of 7.5 m for 2018 to 0.40 and 0.53 for data from a 10 m altitude, for 2018 and 2020, respectively. Model performance was weaker for the last captured tree rows because data coverage was lower. The model underestimated the number of apples per block, which is reasonable, as leaves cover some of the fruits. However, a good relationship to the yield mass per block was found when the estimated apple volume per block was combined with a mean apple density per variety. Overall, coefficients of determination of 0.56 (for the 7.5 m altitude flight) and 0.76 (for the 10 m flights) were achieved. Therefore, we conclude that mapping at an altitude of 10 m performs better than 7.5 m, in the context of low-altitude UAV flights for the estimation of ripe apple parameters directly from 3D RGB dense point clouds.https://www.mdpi.com/2504-446X/9/1/60fruit detectionyield estimationstructure from motion (SfM)unmanned aerial vehicle (UAV)apple trees |
| spellingShingle | Marius Hobart Michael Pflanz Nikos Tsoulias Cornelia Weltzien Mia Kopetzky Michael Schirrmann Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard Drones fruit detection yield estimation structure from motion (SfM) unmanned aerial vehicle (UAV) apple trees |
| title | Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard |
| title_full | Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard |
| title_fullStr | Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard |
| title_full_unstemmed | Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard |
| title_short | Fruit Detection and Yield Mass Estimation from a UAV Based RGB Dense Cloud for an Apple Orchard |
| title_sort | fruit detection and yield mass estimation from a uav based rgb dense cloud for an apple orchard |
| topic | fruit detection yield estimation structure from motion (SfM) unmanned aerial vehicle (UAV) apple trees |
| url | https://www.mdpi.com/2504-446X/9/1/60 |
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