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Monitoring seed maturity is an increasing challenge in agriculture due toclimate change and more restrictive practices. Seeds monitoring in the field isessential to optimize the farming process and to guarantee yield qualitythrough high germination. Traditional methods are based on limited sampling inthe field and analysis in laboratory. Moreover, they are time consuming andonly allow monitoring sub-sections of the crop field. This leads to a lack ofaccuracy on the condition of the crop as a whole due to intra-fieldheterogeneities. Multispectral imagery by UAV allows uniform scan of fields andbetter capture of crop maturity information. On the other hand, deep learningmethods have shown tremendous potential in estimating agronomic parameters,especially maturity. However, they require large labeled datasets. Althoughlarge sets of aerial images are available, labeling them with ground truth is atedious, if not impossible task. In this paper, we propose a method forestimating parsley seed maturity using multispectral UAV imagery, with a newapproach for automatic data labeling. This approach is based on parametric andnon-parametric models to provide weak labels. We also consider the dataacquisition protocol and the performance evaluation of the different steps ofthe method. Results show good performance, and the non-parametric kerneldensity estimator model can improve neural network generalization when used asa labeling method, leading to more robust and better performing deep neuralmodels.

Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images