Plant Identification in Mosaicked Crop Row Images for Automatic Emerged Corn Plant Spacing Measurement

Image processing algorithms for individual corn plant and plant stem center identification were developed. These algorithms were applied to mosaicked crop row image for automatically measuring corn plant spacing at early growth stages. These algorithms utilized multiple sources of information for corn plant detection and plant center location estimation including plant color, plant morphological features, and the crop row centerline. The algorithm was tested over two 41 m (134.5 ft) long corn rows using video acquired two times in both directions. The system had a mean plant misidentification ratio of 3.7%. When compared with manual plant spacing measurements, the system achieved an overall spacing error (RMSE) of 1.7 cm and an overall R2 of 0.96 between manual plant spacing measurement and the system estimates. The developed image processing algorithms were effective in automated corn plant spacing measurement at early growth stages. Interplant spacing errors were mainly due to crop damage and sampling platform vibration that caused mosaicking errors

Reflectance of vegetation, soil, and water

The author has identified the following significant results. Iron deficient and normal grain sorghum plants were sufficiently different spectrally in ERTS-1 band 5 CCT data to detect chlorotic sorghum areas 2.8 acres (1.1 hectares) or larger in size in computer printouts of the MSS data. The ratio of band 5 to band 7 or band 7 minus band 5 relates to vegetation ground cover conditions and helps to select training samples representative of differing vegetation maturity or vigor classes and to estimate ground cover or green vegetation density in the absence of ground information. The four plant parameters; leaf area index, plant population, plant cover, and plant height explained 87 to 93% of the variability in band 6 digital counts and from 59 to 90% of the variation in bands 4 and 5. A ground area 2244 acres in size was classified on a pixel by pixel basis using simultaneously acquired aircraft support and ERTS-1 data. Overall recognition for vegetables, immature crops and mixed shrubs, and bare soil categories was 64.5% for aircraft and 59.6% for spacecraft data, respectively. Overall recognition results on a per field basis were 61.8% for aircraft and 62.8% for ERTS-1 data

Using Aerial Hyperspectral Remote Sensing Imagery to Estimate Corn Plant Stand Density

Since corn plant stand density is important for optimizing crop yield, several researchers have recently developed ground-based systems for automatic measurement of this crop growth parameter. Our objective was to use data from such a system to assess the potential for estimation of corn plant stand density using remote sensing images. Aerial hyperspectral remote sensing imagery was collected on three dates over three plots of corn in central Iowa during the 2004 growing season. The imagery had a spatial resolution of 1 m and a spectral resolution of 3 nm between 498 nm and 855 nm. A machine vision system for early-season measurement of corn plant stand density was also used to map every row of corn within the three plots, and a complete inventory of corn plants was generated as a rich ground reference dataset. A principal component regression analysis was used to assess relationships between plant stand density measurements and principal components of hyperspectral reflectance for each plot, on each image collection date, and at three different spatial resolutions (2, 6, and 10 m). The maximum R2 for regressions was 0.79. Estimates of corn plant stand density were best when using imagery collected at the later vegetative and early reproductive corn growth stages. Quantization effects due to row width complicated corn plant stand density estimates at 2 m spatial resolution, and better estimations were typically seen at resolutions of 6 m and 10 m. Among the different cases of plot, image date, and spatial resolution, the principal components of reflectance most highly correlated with plant stand density were able to be classified into four distinct types, denoted as types A, B, C, and D. Type A principal components contrasted all available visible red wavelengths with all available near-infrared wavelengths. Type B principal components contrasted green wavelengths (531 to 552 nm) plus shorter wave near-infrared (759 nm) with red wavelengths (675 to 693 nm) plus longer wave near-infrared (852 nm). Type C principal components summed green wavelengths (528 to 546 nm) and near-infrared wavelengths (717 to 855 nm). Type D principal components contrasted blue/green wavelengths (498 to 507 nm) with the red edge (717 nm). Remote sensing can be best used to estimate corn plant stand density at mid-season as long as plant stand variability exists and variability due to other factors is minimal

Using Aerial Hyperspectral Remote Sensing Imagery to Estimate Corn Plant Stand Density

Aerial hyperspectral remote sensing imagery was collected on three dates over three plots of corn. The imagery had a spatial resolution of 1 m and a spectral resolution of 3 nm between 471 nm and 828 nm. A machine vision corn plant population sensing system was also used to map every row of corn within the three plots, and a complete inventory of corn plants was generated as a rich ground reference dataset for remote sensing image analysis. A multiple linear regression analysis was performed to estimate corn plant stand density using reflectance in combinations of three wavebands, and R 2 s of up to 0.82 were found. Estimates of corn plant stand density were best when using imagery collected at the later vegetative growth stage. Quantization effects due to row width complicated corn plant stand density estimates at 2 m spatial resolution, and better estimations were typically seen at resolutions of 6 m and 10 m. For the best-case scenarios, the first predictor variable in the regression model typically fell in the blue reflectance region (473 to 492 nm). The second predictor variable was typically in the longer green and shorter red wavelengths (584 to 635 nm), and reflectance for the third predictor variable was typically at the red edge (729 nm) or in the near-infrared region. Because results for the second and third predictor variables tended to straddle between important regions of typical vegetative reflectance spectra, it is expected that multiple linear regressions using a greater number of bands would improve the distinction between important spectral ranges for estimating corn plant stand density

Real-Time Crop Row Image Reconstruction for Automatic Emerged Corn Plant Spacing Measurement

In-field variations in corn plant spacing and population can lead to significant yield differences. To minimize these variations, seeds should be placed at a uniform spacing during planting. Since the ability to achieve this uniformity is directly related to planter performance, intensive field evaluations are vitally important prior to design of new planters and currently the designers have to rely on manually collected data that is very time consuming and subject to human errors. A machine vision-based emerged crop sensing system (ECSS) was developed to automate corn plant spacing measurement at early growth stages for planter design and testing engineers. This article documents the first part of the ECSS development, which was the real-time video frame mosaicking for crop row image reconstruction. Specifically, the mosaicking algorithm was based on a normalized correlation measure and was optimized to reduce the computational time and enhance the frame connection accuracy. This mosaicking algorithm was capable of reconstructing crop row images in real-time while the sampling platform was traveling at a velocity up to 1.21 m s-1 (2.73 mph). The mosaicking accuracy of the ECSS was evaluated over three 40 to 50 m long crop rows. The ECSS achieved a mean distance measurement error ratio of -0.11% with a standard deviation of 0.74%

Maize and sorghum plant detection at early growth stages using proximity laser and time-of-flight sensors

Maize and sorghum are important cereal crops in the world. To increase the maize grain yield, two approaches are used: exploring hybrid maize in plant breeding and improving the crop management system. Plant population is a parameter for calculating the germination rate, which is an important phenotypic trait of seeds. An automated way to obtain the plant population at early growth stages can help breeders to save measuring time in the field and increase the efficiency of their breeding programs. Similar to what has been taking place in production agriculture, plant scientists and plant breeders have been looking for and adopting precision technologies into their research programs; and analyzing plant performance plot-by-plot and even plant-by-plant is becoming the norm and vitally important plant phenomics research and seed industry. Accurate plant location information is needed for determining plant distribution and generating plant stand maps. Two automated plant population detection and location estimation systems using different sensors were developed in this research. A 2D machine vision technique was applied to develop a real-time automatic plant population estimation and plant stand map generation system for maize and sorghum in early growth stages. Laser sensors were chosen as they are not affected by outdoor lighting conditions. Plant detection algorithms were developed based on the unique plant stem structure. Since maize and sorghum look similar at early growth stages, the system was tested over both plants in greenhouse condition. The detection rate of over 93.1% and 83.0% were achieved for maize and sorghum plants from V2 to V6 growth stage, respectively. The mean absolute error and root-mean-error of plant location were 3.1 cm and 3.2 cm m for maize and 2.8 cm and 2.9 cm for grain sorghum plants, respectively. Apart from using laser sensors, a 3D Time-of-Flight camera-based automatic system was also developed for maize and sorghum plant detection at their early growth stages. The images were captured by using a Swift camera from a side-view of the crop row without any shade during the daytime in a greenhouse. A serious of image processing algorithms including point cloud filtering, plant candidate extraction, invalid plant removal, and plant registration were developed for this system. By comparing with the manual measurement, for the maize plant, the average true positive detection rate was 89% with 0.06 standard deviation. For grain sorghum plants, the average true positive detection rate was 85% with 0.08 standard deviation

Field research on the spectral properties of crops and soils, volume 1

The experiment design, data acquisition and preprocessing, data base management, analysis results and development of instrumentation for the AgRISTARS Supporting Research Project, Field Research task are described. Results of several investigations on the spectral reflectance of corn and soybean canopies as influenced by cultural practices, development stage and nitrogen nutrition are reported as well as results of analyses of the spectral properties of crop canopies as a function of canopy geometry, row orientation, sensor view angle and solar illumination angle are presented. The objectives, experiment designs and data acquired in 1980 for field research experiments are described. The development and performance characteristics of a prototype multiband radiometer, data logger, and aerial tower for field research are discussed

Development of a machine vision system for corn plant population, spacing and height measurement

A system was developed to measure the spatial variability of early stage plant population density, spacing and plant height. The Truncated Ellipsoidal (TE) method was developed to segment plants from background. A patch matching algorithm was developed to sequence for video frames of corn row videos. Algorithm performance was analyzed across three tillage treatments, three growth stages from V3 to V8, and three population densities varying from 27,000 to 81,500 plants/ha. Overall, the algorithm estimated the number of plants in 6.1 m crop row lengths with an RMSE of 2.1 plants. Following this encouraging result, a component-based software architecture was developed to automate site specific field data acquisition, processing, and geo-referenced plant parameter extraction. The architecture supported acquisition and processing of different data streams such as digital video or digital serial communications. Based on this architecture, early stage corn population estimation (ESCOPE) software was developed which grabbed pre-recorded digital video from a vehicle-mounted camera that was passed over corn rows and acquired GPS-NMEA strings which were modulated and recorded on the audio channel. Reusability and extensibility characteristics were demonstrated by adding a class to acquire images from the hard drive and also by deriving a new image analyzer class to extract an additional feature. For the crop height measurement, two different sensing approaches, stereo vision and ultrasonic, were investigated as candidate technologies for vehicle-based corn height sensors. For the stereo vision method, a chain code-based stereo correspondence technique was developed to determine the disparity in the stereo image pair. The ultrasonic sensor measured the distance to an object by detecting the time of flight of ultrasonic sound waves. A good correlation was found between the measured and estimated height using both stereo vision and the ultrasonic sensor. For the stereo vision sensor, r2 between the maximum plant height and estimated height was 0.76. For the ultrasonic sensor, r2 between the 25th percentile of the group height statistics and plant collar height was 0.75

Emission and reflection from healthy and stressed natural targets with computer analysis of spectroradiometric and multispectral scanner data

Special emphasis was on corn plants, and the healthy targets were differentiated from stressed ones by remote sensing. Infrared radiometry of plants is reviewed thoroughly with emphasis on agricultural crops. Theory and error analysis of the determination of emittance of a natural target by radiometer is discussed. Experiments were conducted on corn (Zea mays L.) plants with long wavelength spectroradiometer under field conditions. Analysis of multispectral scanner data of ten selected flightlines of Corn Blight Watch Experiment of 1972 indicated: (1) There was no regular pattern of the mean response of the higher level/levels blighted corn vs. lower level/levels blighted corn in any of the spectral channels. (2) The greater the difference between the blight levels, the more statistically separable they usually were in subsets of one, two, three and four spectral channels

Agricultural scene understanding, volume 1

There are no author-identified significant results in this report