Mapping Walnut Water Stress with High Resolution Multispectral UAV Imagery and Machine Learning

Effective monitoring of walnut water status and stress level across the whole orchard is an essential step towards precision irrigation management of walnuts, a significant crop in California. This study presents a machine learning approach using Random Forest (RF) models to map stem water potential (SWP) by integrating high-resolution multispectral remote sensing imagery from Unmanned Aerial Vehicle (UAV) flights with weather data. From 2017 to 2018, five flights of an UAV equipped with a seven-band multispectral camera were conducted over a commercial walnut orchard, paired with concurrent ground measurements of sampled walnut plants. The RF regression model, utilizing vegetation indices derived from orthomosaiced UAV imagery and weather data, effectively estimated ground-measured SWPs, achieving an $R^2$ of 0.63 and a mean absolute error (MAE) of 0.80 bars. The integration of weather data was particularly crucial for consolidating data across various flight dates. Significant variables for SWP estimation included wind speed and vegetation indices such as NDVI, NDRE, and PSRI.A reduced RF model excluding red-edge indices of NDRE and PSRI, demonstrated slightly reduced accuracy ($R^2$ = 0.54). Additionally, the RF classification model predicted water stress levels in walnut trees with 85% accuracy, surpassing the 80% accuracy of the reduced classification model. The results affirm the efficacy of UAV-based multispectral imaging combined with machine learning, incorporating thermal data, NDVI, red-edge indices, and weather data, in walnut water stress estimation and assessment. This methodology offers a scalable, cost-effective tool for data-driven precision irrigation management at an individual plant level in walnut orchards.

Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System

Since the discovery of the first hot Jupiter orbiting a solar-type star, 51 Peg, in 1995, more than 4000 exoplanets have been identified using various observational techniques. The formation process of these sub-Earths remains elusive, and acquiring additional samples is essential for investigating this unique population. In our study, we employ a novel GPU Phase Folding algorithm combined with a Convolutional Neural Network, termed the GPFC method, on Kepler photometry data. This method enhances the transit search speed significantly over the traditional Box-fitting Least Squares method, allowing a complete search of the known KOI photometry data within hours using a commercial GPU card. To date, we have identified five promising sub-Earth short-period candidates: K00446.c, K01821.b, K01522.c, K03404.b, and K04978.b. A closer analysis reveals the following characteristics: K00446.c orbits a K dwarf on a 0.645091-day period. With a radius of $0.461R_\oplus$, it ranks as the second smallest USP discovered to date. K01821.b is a sub-Earth with a radius of $0.648R_\oplus$, orbiting a G dwarf over a 0.91978-day period. It is the second smallest USP among all confirmed USPs orbiting G dwarfs in the NASA Archive. K01522.c has a radius of $0.704 R_\oplus$ and completes an orbit around a Sun-like G dwarf in 0.64672 days; K03404.b, with a radius of $0.738 R_\oplus$, orbits a G dwarf on a 0.68074-day period; and K04978.b, with its planetary radius of $0.912 R_\oplus$, orbits a G dwarf, completing an orbit every 0.94197 days. Three of our finds, K01821.b, K01522.c and K03404.b, rank as the smallest planets among all confirmed USPs orbiting G dwarfs in the Kepler dataset. The discovery of these small exoplanets underscores the promising capability of the GPFC method for searching for small, new transiting exoplanets in photometry data from Kepler, TESS, and future space transit missions.

The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves 97% training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers 100% of known ultra-short-period planets in Kepler light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with Kepler and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.