Spatial and Temporal Variation in an Apple Orchard

Talebpour, Babak; Eminoğlu, Maksut Barış; Yegül, Uğur; Türker, Ufuk

doi:10.21273/hortsci14447-19

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…A similar approach for yield monitoring has been conducted for measuring grapefruit (Peeters et al, 2015;Ünlü et al, 2014). Some studies have also measured individual tree yields for dates (Mazloumzadeh et al, 2010), pears (Perry et al, 2010), apples (Talebpour et al, 2019) and cacao (Carvalho Fig. 4 Visualization of a fill event (i.e., the harvester and tractor travel next to each other in a parallel path while the harvester unloads sugarcane into the wagon/trailer) and the event radius surrounding the harvester, (A) and the resulting yield map (B).…”

Section: Unit-based Yield Monitoringmentioning

confidence: 99%

Yield sensing technologies for perennial and annual horticultural crops: a review

et al. 2022

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Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems.

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Section: Unit-based Yield Monitoringmentioning

confidence: 99%

Yield sensing technologies for perennial and annual horticultural crops: a review

et al. 2022

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Route Optimization in Precision Agriculture Settings: A Multi-Steiner TSP Formulation

Furchi

Lippi

Carpio

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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In this work, we propose a route planning strategy 1 for heterogeneous mobile robots in Precision Agriculture (PA) 2 settings. Given a set of agricultural tasks to be performed 3 at specific locations, we formulate a multi-Steiner Traveling 4 Salesman Problem (TSP) to define the optimal assignment of 5 these tasks to the robots as well as the respective optimal paths 6 to be followed. The optimality criterion aims to minimize the total 7 time required to execute all the tasks, as well as the cumulative 8 execution times of the robots. Costs for travelling from one 9 location to another, for maneuvering and for executing the task 10 as well as limited energy capacity of the robots are considered. 11 In addition, we propose a sub-optimal formulation to mitigate the 12 computational complexity by leveraging the fact that generally 13 in PA settings only a few locations require agricultural tasks in a 14 certain period of interest compared to all possible locations in the 15 field. A formal analysis of the optimality gap between the optimal 16 and the sub-optimal formulations is provided. The effectiveness 17 of the approach is validated in a simulated orchard where three 18 heterogeneous aerial vehicles perform inspection tasks. 19 Note to Practitioners-This paper aims at providing an efficient 20 solution to PA needs by deploying a team of robots able 21 to perform agricultural tasks at given locations in large-scale 22 orchards. In particular, a novel general optimization problem is 23 proposed that, given a set of mobile and possibly heterogeneous 24 robots and a set of agricultural tasks to carry out, defines the 25 assignment of these tasks to the robots as well as the routes to 26 follow, while minimizing the total and the cumulative execution 27 times of the robots. Existing approaches for route optimization 28 in PA generally involves complete coverage of the field by one 29 or multiple robots and do not account for maneuvering costs 30 with general layouts of the field. We consider costs for travelling 31 from one location to another, for executing the task and for 32 maneuvering without any restriction on the layout of the plants 33 as well as we take into account the limited energy capacity of the 34 robots. We also provide a sub-optimal formulation which reduces 35 the computational burden by relaxing the optimization of the 36 maneuvering costs at the locations where agricultural tasks are 37 Manuscript

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Spatial and Temporal Variation in an Apple Orchard

Cited by 2 publications

References 6 publications

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield sensing technologies for perennial and annual horticultural crops: a review

Route Optimization in Precision Agriculture Settings: A Multi-Steiner TSP Formulation

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