Using Apparent Electric Conductivity and NDVI Measurements for Yield Estimation of Processing Tomato Crop

The use of predictive yield maps is an important tool for the delineation of within-field management zones. In particular, appropriate placement in the field of organically grown produce, as is the case for the processing tomato crop in this study, will ensure higher crop yield. Accurate estimation of yield can be used to plan the best time for harvesting and transport for industrial processing. Apparent electrical conductivity (EC_a) and vegetation indices based on crop reflectance are two tools that can be used to help attain these objectives. Developments in the use of sensors have enabled massive georeferenced data sampling of these parameters. The aim of this article is to assess the EC_a and normalized difference vegetation index (NDVI) using geostatistical techniques to optimize their use. Principal component analysis was used to evaluate the predictive yield maps developed. EC_a was a reasonably good indicator of crop production potential throughout the plot as a whole, but NDVI was the best indicator, offering a better resolution than EC_a and a reasonable estimation of yield distribution over the extensive tested crop surface area.