Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy

Abstract

Estimating key crop parameters (e.g., phenology, yield prediction) is a prerequisite for optimizing agrifood supply chains through the use of satellite imagery, but requires timely and accurate crop mapping. The moment in the season and the number of training sites used are two main drivers of crop classification performance. The combined effect of these two parameters was analysed for tomato crop classification, through 125 experiments, using the three main machine learning (ML) classifiers (neural network, random forest, and support vector machine) using a response surface methodology (RSM). Crop classification performance between minority (tomato) and majority (‘other crops’) classes was assessed through two evaluation metrics: Overall Accuracy (OA) and G-Mean (GM), which were calculated on large independent test sets (over 400,000 fields). RSM results demonstrated that lead time and the interaction between the number of majority and minority classes were the two most important drivers for crop classification performance for all three ML classifiers. The results demonstrate the feasibility of preharvest classification of tomato with high performance, and that an RSM-based approach enables the identification of simultaneous effects of several factors on classification performance. SVM achieved the best grading performances across the three ML classifiers, according to both evaluation metrics. SVM reached highest accuracy (0.95 of OA and 0.97 of GM) earlier in the season (low lead time) and with less training sites than the other two classifiers, permitting a reduction in cost and time for ground truth collection through field campaigns.