Agriculture faces the challenge of feeding a growing population with limited or depleting fresh water resources. Advances in irrigation systems and technologies allow site-specific application of irrigation water within the field to improve water use efficiency or reduce water usage for sustainable crop production, especially in arid and semi-arid regions. This paper discusses recent development of variable-rate irrigation (VRI) technologies, data and information for VRI application, and impacts of VRI, including profitability using this technology, with a focus on agronomic factors in precision water management. The development in sprinkler systems enabled irrigation application with greater precision at the scale of individual nozzle control. Further research is required to evaluate VRI prescription maps integrating different soil and crop characteristics in different environments. On-farm trials and whole-field studies are needed to provide support information for practical VRI applications. Future research also needs to address the adjustment of the spatial distribution of prescription zones in response to temporal variability in soil water status and crop growing conditions, which can be evaluated by incorporating remote and proximal sensing data. Comprehensive decision support tools are required to help the user decide where to apply how much irrigation water at different crop growth stages to optimize water use and crop production based on the regional climate conditions and cropping systems.

Machine learning and computer vision technologies based on high-resolution imagery acquired using unmanned aerial systems (UAS) provide a potential for accurate and efficient high-throughput plant phenotyping. In this study, we developed a sorghum panicle detection and counting pipeline using UAS images based on an integration of image segmentation and a convolutional neural networks (CNN) model. A UAS with an RGB camera was used to acquire images (2.7 mm resolution) at 10-m height in a research field with 120 small plots. A set of 1,000 images were randomly selected, and a mask was developed for each by manually delineating sorghum panicles. These images and their corresponding masks were randomly divided into 10 training datasets, each with a different number of images and masks, ranging from 100 to 1,000 with an interval of 100. A U-Net CNN model was built using these training datasets. The sorghum panicles were detected and counted by a predicted mask through the algorithm. The algorithm was implemented using Python with the Tensorflow library for the deep learning procedure and the OpenCV library for the process of sorghum panicle counting. Results showed the accuracy had a general increasing trend with the number of training images. The algorithm performed the best with 1,000 training images, with an accuracy of 95.5% and a root mean square error (RMSE) of 2.5. The results indicate that the integration of image segmentation and the U-Net CNN model is an accurate and robust method for sorghum panicle counting and offers an opportunity for enhanced sorghum breeding efficiency and accurate yield estimation.

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R²), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

Remote sensing can be a rapid, accurate, and simple method for assessing pest damage on plants. The objectives of this study were to identify spectral wavelengths sensitive to cotton aphid infestation. Then, the normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the leaf spectrum were obtained within 350–2500 nm, and their correlation with infestation were qualified. The results showed that leaf spectral reflectance decreased in the visible range (350–700 nm) and the near-infrared range (NIR, 700–1300 nm) as aphid damage severity increased, and significant differences were found in blue, green, red, NIR and short-wave infrared (SWIR) band regions between different grades of aphid damage severity. Decrease in Chlorophyll a (Chl a) pigment was more significant than that in Chlorophyll (Chl b) in the infested plants and the Chl a/b ratio showed a decreasing trend with increase in aphid damage severity. The sensitive spectral bands were mainly within NIR and SWIR ranges. The best spectral indices NDSI (R₆₇₈, R₁₄₇₁) and RSI (R₁₉₇₅, R₁₉₀₄) were formulated with these sensitive spectral regions through reducing precise sampling method. These new indices along with 16 other stress related indices compiled from literature were further tested for their ability to detect aphid damage severity. The two indices in this study showed significantly higher coefficients of determination (R² of 0.81 and 0.81, p < 0.01) and the least RMSE values (RMSE of 0.50 and 0.49), and hence have potential application in assessing aphid infestation severity in cotton.

Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (T_s) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R² (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale.