Generative-Model-Based Data Labeling for Deep Network Regression: Application to Seed Maturity Estimation from UAV Multispectral Images

Abstract

Field seed maturity monitoring is essential to optimize the farming process and guarantee yield quality through high germination. Remote sensing of parsley fields through UAV multispectral imagery allows uniform scanning and better capture of crop information, in comparison to traditional limited field sampling analysis in the laboratory. Moreover, they only represent localized sub-sections of the crop field and are time consuming to process. The limited availability of seed sample maturity data is a drawback for applying deep learning methods, which have shown tremendous potential in estimating agronomic parameters, especially maturity, as they require large labeled datasets. In this paper, we propose a parametric and non-parametric-based weak labeling approach to overcome the lack of maturity labels and render possible maturity estimation by deep network regression to assist growers in harvest decision-making. We present the data acquisition protocol and the performance evaluation of the generative models and neural network architectures. Convolutional and recurrent neural networks were trained on the generated labels and evaluated on maturity ground truth labels to assess the maturity quantification quality. The results showed improvement by the semi-supervised approaches over the generative models, with a root-mean-squared error of 0.0770 for the long-short-term memory network trained on kernel-density-estimation-generated labels. Generative-model-based data labeling can unlock new possibilities for remote sensing fields where data collection is complex, and in our usage, they provide better-performing models for parsley maturity estimation based on UAV multispectral imagery.

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

“…To address this issue, scholars have introduced TF to mitigate the saturation phenomenon [60]. When crops reach a high canopy cover, the changes in canopy structure are not as pronounced as those in the early growth stages over a considerable period [61,62]. Shallow texture features may not effectively capture such changes.…”

“…Numerous studies indicate that deep learning can better explore the latent deep features in images. However, most studies tend to investigate the combined effects of deep learning models with original data imagery for estimating crop parameters [40,61,[63][64][65][66]. Overlooking the contribution of the deep information contained in texture images.…”

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

“…For widespread adoption in agriculture, it is essential to rely on measured data and integrate sources to ensure practical robustness [10]. Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MS) sensors offer several benefits in precision agriculture, enabling the acquisition of high-resolution data that capture the spatial variability of attributes and crops [11][12][13]. Cao et al [14] compared both RGB and multispectral imagery from UAV to map Stay Green (SG) phenotyping of diversified wheat germplasm.…”