Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Yield mapping methods for manually harvested crops A.F. Colaço a,b, ⁎ , R.G. Trevisan a,c , F.H.S. Karp a,d , J.P. Molin a a Biosystems Engineering Department, University of São Paulo, Av. Pádua Dias 11, 13418-900 Piracicaba, São Paulo, Brazil b CSIRO, Waite Campus, Locked Bag 2, Glen Osmond, SA 5064, Australia 1 c Department of Crop Sciences, University of Illinois at Urbana-Champaign, W201 Turner Hall, 1102 S. Goodwin Avenue, Urbana, IL 61801, USA 1 d School of Plant, Environmental and Soil Sciences, Louisiana State University, 104 Sturgis Hall, 137 J.C. Miller Hall, Baton Rouge, LA 70803, USA 1 ARTICLE INFO Keywords: Yield spatial variability Yield map Site-specific management Fruit crops ABSTRACT Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the dis- tribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha -1 , depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management. 1. Introduction The yield map is a key information layer for Precision Agriculture (PA) systems. This information indicates regions of the field with dif- ferent performance, which might demand different management stra- tegies. In many mechanically harvested crops, the data to generate yield maps comes directly from onboard yield monitors. This equipment is installed on the harvest machine and collects geographic coordinates (by a GNSS receiver - Global Navigation Satellite System) and local yield of the crop (generally by means of a flow sensor inside the har- vester). This type of yield mapping has been reported since the late 1980s (Schueller and Bae, 1987; Searcy et al., 1989) and today, yield monitors are available in most modern harvesters. Since its creation, this technology has promoted PA initiatives in grain crops by helping farmers to understand and manage the spatial variability of their fields. According to Bramley and Ouzman (2018), access to yield mapping can also promote the adoption of other PA technologies such as crop sensing and site-specific management. Unlike in modern grain crop production, onboard yield monitors are not a possibility in manually harvested crops, such as many fruit and vegetable crops. Thus, yield mapping techniques must rely on a manual harvest procedure. Yield mapping is therefore not common, and neither is site-specific management more broadly. It can be suggested that fruit and vegetable crops that are manually harvested have been neglected from PA development due to lack of research regarding aspects of yield mapping. Crops such as citrus and apple cover extensive areas throughout the world - e.g. orange in Brazil (630,000 ha) and USA (215,000 ha), apple in Europe (over 950,000 ha) and China (over 2,200,000 ha) (FAO, 2020) - and would greatly benefit from yield mapping techniques that are assessable and reliable. In manually harvested crops, the handpicked produce is often briefly stored at their harvest site in some kind of container (bins, bags, boxes, etc.) before they are loaded and transported. In broad-acre horticultural crops destined for the processing industry (e.g. for fruit juice extraction), larger containers or ‘big bags’ are preferred over small boxes, which are normally preferred in small orchards and for the harvest of fruits and vegetables for the ‘fresh’ market. In both har- vesting types, the yield map can be produced by georeferencing these https://doi.org/10.1016/j.compag.2020.105693 Received 3 June 2020; Received in revised form 29 July 2020; Accepted 1 August 2020 ⁎ Corresponding author at: CSIRO, Waite Campus, Locked Bag 2, Glen Osmond, SA 5064, Australia. E-mail address: andre.colaco@csiro.au (A.F. Colaço). 1 Present address. Computers and Electronics in Agriculture 177 (2020) 105693 0168-1699/ © 2020 Elsevier B.V. All rights reserved. T