Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting by Tantan Jin, Xiongzhe Han, Pingan Wang, Zhao Zhang, Jie Guo, Fan Ding

“…In outdoor field experiments conducted under cloudy, low-light, and artificial lighting conditions, the model achieved localization errors of 2.43 mm (X-axis), 3.70 mm (Y-axis), and 1.28 mm (Z-axis), representing reductions of 19.27 %, 12.67 %, and 23.05 %, respectively. …”
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Assessment of GOCI-II satellite remote sensing products in Lake Taihu by Min Zhao, Min Zhao, Huaming Li, Hao Li, Xuan Zhang, Xiaosong Ding, Xiaosong Ding, Fang Gong

“…Compared to NIR-derived products, the UV algorithm showed improved accuracy for Chl-a concentration, TSM concentration, and aph, with reductions in APD values by 16.92%, 3.32%, and 10.91%, respectively. …”
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Unveiling the performance and influential factors of GEDI L2A for building height retrieval by Peimin Chen, Huabing Huang, Peng Qin, Zhenbang Wu, Zixuan Wang, Chong Liu, Na Dong, Jie Wang

“…To ensure data reliability, we refined the GEDI dataset by excluding footprints outside buildings, filtering out low-quality footprints, removing footprints failing to detect ground elevation using the interquartile range (IQR) detection method, and excluding footprints with geolocation errors through an eight-direction offset approach. …”
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NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

“…In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. …”
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