Algorithmic geolocation of harvest in hand-picked agriculture
Algorithmic geolocation of harvest in hand-picked agriculture
复制标题
手工采摘农业收获的算法地理定位
DOI：
10.1111/nrm.12158
复制
发表时间：
2018
影响因子：
1.6
通讯作者：
Sowers, Richard B.
中科院分区：
文献类型：
--
作者：
Srivastava, Nitin;Maneykowski, Peter;Sowers, Richard B.
精确农业在很大程度上依赖于测量产量;这允许反馈来优化各种决策。虽然空间颗粒产量映射在机收行作物中很容易实现，但在手工采摘的行作物中则更困难。我们在这里研究草莓收获过程中收集的数据集;使用智能手机，我们收集了各个收割机的全球定位系统（GPS）日志。利用特征识别的最新进展，我们能够在算法上将路径分解为进入田地的个人游览以收获浆果。这为产量制图奠定了基础。为了进一步发展这一领域，我们建议资源管理者对手工采摘作物的地理定位收获数据收集进行更大规模的试验。将地理定位收获数据与田间作业的其他方面的数据结合起来。将地理定位收获数据与质量和数量等产出测量结合起来。
AbstractPrecision agriculture significantly depends on measuring yield; this allows feedback to optimize various decisions. While spatially granular yield mapping is readily available in machine‐harvested row crops, it is more difficult in hand‐picked row crops. We study here a data set collected during harvesting of strawberries; using smartphones, we collected Global Positioning System (GPS) logs of individual harvesters. Using recent advances in feature identification, we are able to algorithmically decompose the path into individual excursions into the field to harvest the berries. This lays the groundwork for yield mapping.To further develop this area, we recommend that Resource ManagersPursue wider scale trials of geolocated harvest data collection of hand‐picked crops.Join this geolocated harvest data with data from other aspects of field operations.Join this geolocated harvest data with output measurements like quality and quantity.