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1. Detecting intra-field variation in rice yield with UAV imagery and deep learning

   https://doi.org/10.3389/fpls.2022.716506  

   Bellis, E.; Hashem, A.; Causey, J; Runkle, B.R.; Moreno-Garcia, B.; Burns, B; Green, V.S.; Burcham, T.N.; Reba, M.L.; Huang, X. (March 2022, Frontiers in plant science)

   Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice. 

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2. Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX

   https://doi.org/10.3390/su12176822  

   Carroll, Sam R.; Le, Kieu Ngoc; Moreno-García, Beatriz; Runkle, Benjamin R. (September 2020, Sustainability)

   null (Ed.)

   With population growth and resource depletion, maximizing the efficiency of soybean (Glycine max [L.] Merr.) and rice (Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R2 values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated. 

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3. Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis

   Mao, Hanzi; Liu, Xi; Duffield, Nick; Yuan, Hao; Ji, Shuiwang Ji; Mohanty, Binayak (January 2020, International Conference on Data Mining)

   null (Ed.)

   Abstract—The emergence of remote sensing technologies cou- pled with local monitoring workstations enables us the un- precedented ability to monitor the environment in large scale. Information mining from multi-channel geo-spatiotemporal data however poses great challenges to many computational sustainability applications. Most existing approaches adopt various dimensionality reduction techniques without fully taking advantage of the spatiotemporal nature of the data. In addition, the lack of labeled training data raises another challenge for modeling such data. In this work, we propose a novel semi-supervised attention-based deep representation model that learns context-aware spatiotemporal representations for prediction tasks. A combination of convolutional neural networks with a hybrid attention mechanism is adopted to extract spatial and temporal variations in the geo-spatiotemporal data. Recognizing the importance of capturing more complete temporal dependencies, we propose the hybrid attention mechanism which integrates a learnable global query into the classic self-attention mechanism. To overcome the data scarcity issue, sampled spatial and temporal context that naturally reside in the largely-available unlabeled geo-spatiotemporal data are exploited to aid meaningful representation learning. We conduct experiments on a large-scale real-world crop yield prediction task. The results show that our methods significantly outperforms existing state-of-the-art yield prediction methods, especially under the stress of training data scarcity. 

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4. Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

   https://doi.org/10.1038/s41598-021-86480-z  

   Falco, Nicola; Wainwright, Haruko M.; Dafflon, Baptiste; Ulrich, Craig; Soom, Florian; Peterson, John E.; Brown, James Bentley; Schaettle, Karl B.; Williamson, Malcolm; Cothren, Jackson D.; et al (December 2021, Scientific Reports)

   Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management. 

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5. An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

   https://doi.org/10.3390/su151712908  

   Zhao, Haoteng; Di, Liping; Guo, Liying; Zhang, Chen; Lin, Li (September 2023, Sustainability)

   Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making. 

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