Estimating corn emergence date using UAV-based imagery

Assessing corn (Zea Mays L.) emergence uniformity soon after planting is important for relating to grain production and for making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been used for determining corn densities at vegetative growth stage 2 (V2) and later, but not as a tool for detecting emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery. A field experiment was designed with four planting depths to obtain a range of corn emergence dates. UAV imagery was collected during the first, second and third weeks after emergence. Acquisition height was approximately 5m above ground level resulted in a ground sampling distance 1.5 mm pixel-1. Seedling size and shape features derived from UAV imagery were used for DAE classification based on the Random Forest machine learning model. Results showed image features were distinguishable for different DAE (single day) within the first week after initial corn emergence with a moderate overall classification accuracy of 0.49. However, for the second week and beyond the overall classification accuracy diminished (0.20 to 0.35). When estimating DAE within a three-day window (± 1 DAE), overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and major axis length/area were important image features to predict corn DAE. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date to assist in establishing emergence uniformity. Additional studies are needed for fine-tuning image collection procedures and image feature identification in order to improve accuracy

Evaluating Geometric Measurement Accuracy Based on 3D Reconstruction of Automated Imagery in a Greenhouse

Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method

Quantifying corn emergence using UAV imagery and machine learning

Corn (Zea mays L.) is one of the important crops in the United States for animal feed, ethanol production, and human consumption. To maximize the final corn yield, one of the critical factors to consider is to improve the corn emergence uniformity temporally (emergence date) and spatially (plant spacing). Conventionally, the assessment of emergence uniformity usually is performed through visual observation by farmers at selected small plots to represent the whole field, but this is limited by time and labor needed. With the advance of unmanned aerial vehicle (UAV)-based imaging technology and advanced image processing techniques powered by machine learning (ML) and deep learning (DL), a more automatic, non-subjective, precise, and accurate field-scale assessment of emergence uniformity becomes possible. Previous studies had demonstrated the success of crop emergence uniformity using UAV imagery, specifically at fields with simple soil background. There is no research having investigated the feasibility of UAV imagery in the corn emergence assessment at fields of conservation agriculture that are covered with cover crops or residues to improve soil health and sustainability. The overall goal of this research was to develop a fast and accurate method for the assessment of corn emergence using UAV imagery, ML and DL techniques. The pertinent information is essential for corn production early and in-season decision making as well as agronomy research. The research comprised three main studies, including Study 1: quantifying corn emergence date using UAV imagery and a ML model; Study 2: estimating corn stand count in different cropping systems (CS) using UAV images and DL; and Study 3: estimating and mapping corn emergence under different planting depths. Two case studies extended Study 3 to field-scale applications by relating emergence uniformity derived from the developed method to planting depths treatments and estimating final yield. For all studies, the primary imagery data were collected using a consumer-grade UAV equipped with a red-green-blue (RGB) camera at a flight height of approximate 10 m above ground level. The imagery data had a ground sampling distance (GSD) of 0.55 - 3.00 mm pixel-1 that was sufficient to detect small size seedlings. In addition, a UAV multispectral camera was used to capture corn plants at early growth stages (V4, V6, and V7) in case studies to extract plant reflectance (vegetation indices, VIs) as plant growth variation indicators. Random forest (RF) ML models were used to classify the corn emergence date based on the days after emergence (DAE) to time of assessment and estimate yield. The DL models, U-Net and ResNet18, were used to segment corn seedlings from UAV images and estimate emergence parameters, including plant density, average DAE (DAEmean), and plant spacing standard deviation (PSstd), respectively. Results from Study 1 indicated that individual corn plant quantification using UAV imagery and a RF ML model achieved moderate classification accuracies of 0.20 - 0.49 that increased to 0.55 - 0.88 when DAE classification was expanded to be within a 3-day window. In Study 2, the precision for image segmentation by the U-Net model was [greater than or equal to] 0.81 for all CS, resulting in high accuracies in estimating plant density (R2 [greater than or equal to] 0.92; RMSE [less than or equal to] 0.48 plants m-1). Then, the ResNet18 model in Study 3 was able to estimate emergence parameters with high accuracies (0.97, 0.95, and 0.73 for plant density, DAEmean, and PSstd, respectively). Case studies showed that crop emergence maps and evaluation in field conditions indicated an expected trend of decreasing plant density and DAEmean with increasing planting depths and opposite results for PSstd. However, mixed trends were found for emergence parameters among planting depths at different replications and across the N-S direction of the fields. For yield estimation, emergence data alone did not show any relation with final yield (R2 = 0.01, RMSE = 720 kg ha-1). The combination of VIs from all the growth stages was only able to estimate yield with R2 of 0.34 and RMSE of 560 kg ha-1. In summary, this research demonstrated the success of UAV imagery and ML/DL techniques in assessing and mapping corn emergence at fields practicing all or some components of conservation agriculture. The findings give more insights for future agronomic and breeding studies in providing field-scale crop emergence evaluations as affected by treatments and management as well as relating emergence assessment to final yield. In addition, these emergence evaluations may be useful for commercial companies when needing justification for developing new technologies relating to precision planting to crop performance. For commercial crop production, more comprehensive emergence maps (in terms of temporal and spatial uniformity) will help to make better replanting or early management decisions. Further enhancement of the methods such as more validation studies in different locations and years as well as development of interactive frameworks will establish a more automatic, robust, precise, accurate, and 'ready-to-use' approach in estimating and mapping crop emergence uniformity.Includes bibliographical references