Yield mapping methods for manually harvested 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 distribution 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.