Unmanned aerial systems (UASs) have increased our capacity for collecting finer spatiotemporal resolution data that were previously unobtainable through conventional methods. The use of UAS for obtaining high‐throughput phenotyping (HTP) data in plant breeding programs has gained popularity in recent years. The integrity and quality of the raw data are essential for ensuring the accuracy of predictive tools and proper interpretation of the data. This paper summarizes the standard operation procedures for high‐quality UAS data collection, processing, and analysis for UAS‐based HTP (UAS‐HTP). Plant breeders can follow these procedures to implement a UAS‐HTP system in their germplasm enhancement and cultivar development programs.
Thanks to sensor developments, unmanned aircraft system (UAS) are the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications in these days. UAS imagery datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS and sensors. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm to generate 3D point clouds, orthomosaic images, and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel computing to process big UAS data faster for certain applications. This study evaluated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for performance evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the most powerful machine in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in speed-up and efficiency metrics for scalability, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system could be a good alternative computing environment in UAS data processing for agricultural applications.
Cotton (Gossypium spp.) is one of the important cash crops in the United States. Monitoring in-season growth metrics, from early season growth to harvest, is crucial for predictive and prescriptive cotton farming. In recent years, forecasting models have garnered considerable attention to predict canopy indices. This allows selection of management options during crop growth to boost cotton yield and profitability. Here, we used unmanned aerial system-derived canopy features, including canopy cover, canopy height, and excess green index, collected from 3500 plots at Driscoll in Corpus Christi, Texas during the years 2019, 2020, and 2021 for in-season growth forecasting. Training datasets in our model were produced by K-Means clustering and Dynamic Time Warping (DTW) techniques were used to compare various Long Short-Term Memory (LSTM) models in predicting the three canopy features. Accuracy was determined using Root Mean Square Error (RMSE). Results indicated higher predictive capacity of Convolutional Neural Networks (CNN) LSTM for canopy cover, and multi-layer stacked LSTMs for canopy height and excess green index respectively. Overall, results show tremendous potential for in-season growth forecasting and management of agricultural inputs like pesticides and fertilizers for improving crop health and productivity.
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