Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method

As the second largest rice producer, India contributes about 20% of the world’s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (R2 = 0.81, RMSE = 0.41 t/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (R2 ≈ 0.78) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIRV was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models

Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method

As the second largest rice producer, India contributes about 20% of the world’s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (R2 = 0.81, RMSE = 0.41 t/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (R2 ≈ 0.78) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIRV was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models

Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles

Fixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km2) environmental monitoring and small-area (2) fine vegetation surveys, respectively, having different characteristics in terms of flight cost, operational efficiency, and landing and take-off methods. However, large-area fine mapping in complex forest environments is still a challenge in UAV remote sensing. Here, we developed a method that combines a multi-rotor UAV and a fixed-wing UAV to solve this challenge at a low cost. Firstly, we acquired small-scale, multi-season ultra-high-resolution red-green-blue (RGB) images and large-area RGB images by a multi-rotor UAV and a fixed-wing UAV, respectively. Secondly, we combined the reference data of visual interpretation with the multi-rotor UAV images to construct a semantic segmentation model and used the model to expand the reference data. Finally, we classified fixed-wing UAV images using the large-area reference data combined with the semantic segmentation model and discuss the effects of different sizes. Our results show that combining multi-rotor and fixed-wing UAV imagery provides an accurate prediction of tree species. The model for fixed-wing images had an average F1 of 92.93%, with 92.00% for Quercus wutaishanica and 93.86% for Juglans mandshurica. The accuracy of the semantic segmentation model that uses a larger size shows a slight improvement, and the model has a greater impact on the accuracy of Quercus liaotungensis. The new method exploits the complementary characteristics of multi-rotor and fixed-wing UAVs to achieve fine mapping of large areas in complex environments. These results also highlight the potential of exploiting this synergy between multi-rotor UAVs and fixed-wing UAVs

Genome-Wide Analysis of the Biosynthesis and Deactivation of Gibberellin-Dioxygenases Gene Family in Camellia sinensis (L.) O. Kuntze

Gibberellins (GAs), a class of diterpenoid phytohormones, play a key role in regulating diverse processes throughout the life cycle of plants. Bioactive GA levels are rapidly regulated by Gibberellin-dioxygenases (GAox), which are involved in the biosynthesis and deactivation of gibberellin. In this manuscript, a comprehensive genome-wide analysis was carried out to find all GAox in Camellia sinensis. For the first time in a tea plant, 14 CsGAox genes, containing two domains, DIOX_N (PF14226) and 2OG-FeII_Oxy, were identified (PF03171). These genes all belong to 2-oxoglutarate-dependent dioxygenases (2-ODD), including four CsGA20ox (EC: 1.14.11.12), three CsGA3ox (EC: 1.14.11.15), and seven CsGA2ox (EC: 1.14.11.13). According to the phylogenetic classification as in Arabidopsis, the CsGAox genes spanned five subgroups. Each CsGAox shows tissue-specific expression patterns, although these vary greatly. Some candidate genes, which may play an important role in response to external abiotic stresses, have been identified with regards to patterns, such as CsGA20ox2, CsGA3ox2, CsGA3ox3, CsGA2ox1, CsGA2ox2, and CsGA2ox4. The bioactive GA levels may be closely related to the GA20ox, GA3ox and GA2ox genes. In addition, the candidate genes could be used as marker genes for abiotic stress resistance breeding in tea plants