CROP AUTOMATED RELATIVE MATURITY SYSTEM

Abstract

An automated relative maturity system for measuring the relative maturity of a large number of plots of diverse varieties of plants growing in a field or fields. A field to be evaluated is laid out in multiple plots with a specific variety assigned to a preselected plot or plots and with areas set aside throughout the field for planting of check varieties of known relative maturity. High-precision GPS is used with a planter to record the location of each plot within the field on a map. When leaf senescence is under way throughout the field, a radiometric crop sensor mounted on a vehicle also equipped with high-precision GPS is used to scan the plants in the plots to record readings of the plants synchronized to the GPS map locations, including the check plants of known relative maturity. Software is used to calculate the relative maturity of each variety.

Description

CROSS REFERENCE TO RELATED APPLICATION

This application claims benefit of U.S. Provisional Application Ser. No. 61/235908 filed Aug. 21, 2009 and U.S. Provisional Application Ser. No. 61/349018 filed May 27, 2010 and U.S. Provisional Application Ser. No. 61/373471 filed Aug. 13, 2010 which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

The present invention relates generally to a system for measuring relative maturity of a crop and, more specifically, to an automated relative maturity system for measuring quickly and efficiently relative maturity of a large number of plants of diverse varieties.

The growing season for agricultural crops varies from location to location. In the United States, the growing season is longer the farther south the crop growing location and shorter the farther north the crop growing location. While there is no standardized maturity zone map, for soybeans, most divide the United States into eleven or twelve maturity zones. Farmers can improve their opportunity for high yields by planting seed of varieties that have maturities adapted to the growing season at the farmer's location. Accordingly, the seed of varieties of most major crops, including corn, rapeseed (or canola), soybeans, sunflower, and wheat, are sold by seed companies primarily into the maturity zone corresponding to the relative maturity of the variety. In the development of novel crop varieties, relative maturity is a critical characteristic that is tracked and measured by the seed companies.

In the past, maturity was measured manually. Workers would walk through fields having multiple plots of varieties under development as the plants neared maturity and, based on a visual evaluation of the plants, come up with a subjective maturity of plants in each of the plots relative to other plants in the field, typically including a number of check plants of known maturity. Collecting data using this method is very time consuming. In addition, despite best efforts, there is inevitably variation in each data collector's subjective evaluation of maturity and also a tendency even among individual data collectors to alter a subjective evaluation of maturity, especially between fields.

There is, accordingly, a need for a high-speed, automated and more objective system for measuring relative maturity of diverse varieties of plants growing in one or multiple fields.

SUMMARY OF THE INVENTION

The present invention consists of an automated relative maturity system for measuring the relative maturity of a large number of plots of diverse varieties of plants growing in a field or fields. A field to be evaluated is laid out in multiple plots with a specific variety assigned to a preselected plot or plots and with areas set aside throughout the field for planting of check varieties of known relative maturity. High-precision GPS is used with a planter to record the location of each plot within the field. At a selected time in the life cycle of the crop, preferably when leaf senescence is under way throughout the field, a radiometric crop sensor mounted on a vehicle is used to scan the plants in the plots to record readings of the plants synchronized to the GPS map locations, including the check plants of known relative maturity. Software is used to calculate the relative maturity of each variety. In a preferred embodiment, this relative maturity data is passed on to a database of other characteristics of each individual variety evaluated in the field.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a representation of a map of a field in which pluralities of varieties of a crop are to be planted.

FIG. 2 is a representation of the field of FIG. 1 showing the path of a sensor transport traversing the field.

FIG. 3 is a front view of a sensor transport used in the present invention for moving a pair of radiometric sensors over plants in the field.

FIG. 4 is a rear side view of the sensor transport of FIG. 3.

FIG. 5 is a diagram showing the arrangement of plots of Field A.

FIG. 6 is a diagram showing the arrangement of plots of Field C.

FIG. 7 is a diagram showing a 48 range by 54 row section of Field A, check strips planted in rows 1-6 and 49-54 and experimental varieties in rows 7-48.

FIG. 8 is a diagram showing a 48 range by 52 row section of Field C, check strips planted in rows 1-4 and 49-52 and experimental varieties in rows 5-48.

FIG. 9 is a chart of radiometric relative maturity value vs. NDVI using the data from Table 3.

FIG. 10 is a chart of radiometric relative maturity value vs. NDVI using the data from Table 4.

FIG. 11a Photograph of above canopy active sensor method for corn staygreen phenotyping methodology

FIG. 11b Photograph of Corn Staygreen phenotyping methodology below canopy active sensor method

FIG. 12a Graph depicting average staygreen visual readings and active sensor readings across 631 and 641

FIG. 12b Graph depicting average staygreen visual readings and active sensor readings across 631 and 641

FIG. 13a Graph depicting correlation of visual to active sensor readings, peak on week 5

FIG. 13b Graph depicting correlation of hybrid rankings, visual to active sensor, stable for two weeks, 4 and 5

FIG. 13c Graph depicting hybrid—60% visual staygreen is a good indicator for center of two week stable scanning period

FIG. 13d Graph depicting comparison of above and below scanning methods the below good on first date above best and stable for two weeks, 4 and 5

FIG. 13e Graph depicting inbred correlation of visual to active sensor readings, peak on week 5

FIG. 13f Graph depicting correlation of inbred rankings, visual to active sensor, relatively stable for three weeks, week 3, 4 and 5

FIG. 13g Graph depicting inbred 50% visual staygreen is a good indicator for center of three week stable scanning period

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Plants absorb and reflect specific wavelengths of light across the spectrum of natural light. The pattern of reflectance and absorbance changes through the life cycle of the plant. Indices comprised of specific wavelengths of reflected energy correlate with the condition of the plant. For example, spongy mesophyll leaf tissue has a high reflectance in the near infrared (NIR) generally defined as the range between approximately 700 and 1000 nm. Since the spongy mesophyll section of the leaf is structurally stable in a healthy leaf and will have a relatively high reflectance in the NIR, whereas the leaf tissue of plants undergoing senescence will have increasingly reduced reflectance in the NIR. The chlorophyll in plants has a high absorbance in the range of between approximately 400 to 500 and 600 to 700 nm, referred to herein as blue and red light, respectively. Accordingly, as the amount of chlorophyll in the plant tissue decreases over time during senescence, the relative absorbance of visual light will decrease.

The apparatus and methodologies described herein utilize radiometric crop sensors that measure the reflectance and absorbance of one or more frequencies of light by plant tissues. There are two types of radiometric sensors, active sensors which use one or more internal light sources to illuminate the plants being evaluated, and passive sensors which use ambient light only. A preferred sensor is the GreenSeeker® RT100 sold by NTech Industries (Ukiah, Calif.) a Trimble Navigation Limited Company, Sunnyvale, Calif. While the sensors disclosed in the preferred embodiments utilize active light sources, so-called passive sensors that utilize ambient light may also be used. Such sensors may be adapted to use visual light, most commonly the red and NIR wavelengths to generate information about the conditions of plants. A commonly used index in assessing crop conditions is the normalized difference vegetative index (NDVI). The NDVI was developed during early use of satellites to detect living plants remotely from outer space. The index is defined as

%NDVI=(NIR−V)/(NIR+V)×100

where NIR is the reflectance in the NIR range and V is the reflectance in the visual range. Preferred sensors for use with the present invention generate an output that is in NDVI units.

The apparatus and methodologies described herein make advantageous use of the Global Positioning Satellite (GPS) system to determine and record the positions of fields, plots within the fields and plants within the plots and to correlate collected plant condition data. Although the various methods and apparatus will be described with particular reference to GPS satellites, it should be appreciated that the teachings are equally applicable to systems which utilize pseudolites or a combination of satellites and pseudolites. Pseudolites are ground- or near ground-based transmitters which broadcast a pseudorandom (PRN) code (similar to a GPS signal) modulated on an L-band (or other frequency) carrier signal, generally synchronized with GPS time. Each transmitter may be assigned a unique PRN code so as to permit identification by a remote receiver. The term “satellite”, as used herein, is intended to include pseudolites or equivalents of pseudolites, and the term GPS signals, as used herein, is intended to include GPS-like signals from pseudolites or equivalents of pseudolites.

It should be further appreciated that the methods and apparatus of the present invention are equally applicable for use with the GLONASS and other satellite-based positioning systems. The GLONASS system differs from the GPS system in that the emissions from different satellites are differentiated from one another by utilizing slightly different carrier frequencies, rather than utilizing different pseudorandom codes. As used herein and in the claims which follow, the term GPS should be read as indicating the United States Global Positioning System as well as the GLONASS system and other satellite- and/or pseudolite-based positioning systems.

FIG. 1 illustrates an agricultural field 10 which has been planted in accordance with the methods described herein. A planter equipped with a high-precision GPS receiver results in the development of a digital map of the agricultural field 10. The map defined through this operation may become the base map and/or may become a control feature for a machine guidance and/or control system to be discussed in further detail below. The map should be of sufficient resolution so that the precise location of a vehicle within the area defined by the map can be determined to a few inches with reference to the map. Currently available GPS receivers, for example as the ProPak®-V3produced by NovAtel Inc. (Calgary, Alberta, Canada) are capable of such operations.

For the operation, a tractor or other vehicle is used to tow a planter across the field 10. The planter is fitted with a GPS receiver which receives transmissions from GPS satellites and a reference station. Also on-board the planter is a monitoring apparatus which records the position of seeds as they are planted by the planter. In other words, using precise positioning information provided by the GPS receiver and an input provided by the planter, the monitoring apparatus records the location at which each seed is deposited by the planter in the field 10.

As the tractor and planter proceeds across field 10 to plant various rows of seeds or crops, a digital map is established wherein the location of each seed planted in field 10 is stored. Such a map or other data structure which provides similar information may be produced on-the-fly as planting operations are taking place. Alternatively, the map may make use of a previously developed map (e.g., one or more maps produced from earlier planting operations, etc.). In such a case, the previously stored map may be updated to reflect the position of the newly planted seeds. Indeed, in one embodiment a previously stored map is used to determine the proper location for the planting of the seeds/crops.

In such an embodiment, relevant information stored in a database, for example the location of irrigation systems and/or the previous planting locations of other crops, may be used to determine the location at which the new crops/seeds should be planted. This information is provided to the planter (e.g., in the form of radio telemetry data, stored data, etc.) and is used to control the seeding operation. As the planter (e.g., using a conventional general purpose programmable microprocessor executing suitable software or a dedicated system located thereon) recognizes that a planting point is reached (e.g., as the planter passes over a position in field 10 where it has been determined that a seed should be planted), an onboard control system activates a seed planting mechanism to deposit the seed. The determination as to when to make this planting is made according to a comparison of the planter's present position as provided by the GPS receiver and the seeding information from the database. For example, the planting information may accessible through an index which is determined according to the planter's current position (i.e., a position-dependent data structure). Thus, given the planter's current location, a look-up table or other data structure can be accessed to determine whether a seed should be planted or not.

In cases where the seeding operation is used to establish the digital map, the seeding data need not be recorded locally at the planter. Instead, the data may be transmitted from the planter to some remote recording facility (e.g., a crop research station facility or other central or remote workstation location) at which the data may be recorded on suitable media. The overall goal, at the end of the seeding operation, is to have a digital map which includes the precise position (e.g., to within a few inches) of the location of each seed planted. As indicated, mapping with the GPS technology is one means of obtaining the desired degree of accuracy.

The development of novel varieties of crops typically involves growing a large number of varieties side-by-side in research fields in what are sometimes called preliminary yield trials. A common arrangement is to plant each individual variety in a plot that includes a sufficient number of plants to generate valid data, leaving space between plots for access by workers and field equipment. In a preferred embodiment, the field 10 is divided into a plurality of rectangular plots 12 divided by unplanted rows 14 and 16. To provide a standard or check for the determination of relative maturity, check plots 18 are included. The check plots 18 are planted with varieties of known maturity to be used as a comparison for the relative maturity of plants in the research plots. Preferably, a number of different check varieties are planted to provide a range of maturities to span the expected maturities of the plants in the research plots.

In a preferred embodiment, the field 10 is organized in non-replicated blocks of 2122 plots 12. Most of the plots are planted with experimental varieties of similar parentage and a smaller number of the plots are planted with a selection of different check varieties (FIGS. 7 and 8). Each plot 12 has one row with a planting density of 10 seeds per foot and is approximately 7 feet long. The unplanted rows 14 and 16 provide approximately three feet of walkway/vehicle access. It is common to plant experimental varieties having an expected range of maturities covering no more than three maturity zones so that all plants will reach maturity within approximately a one-month period. For soybeans, maturity is generally defined as plants having dropped all leaves and with 95% of pods having a mature brown color.

As result of the mapping operation conducted during planting, the latitude and longitude location of each plant in the field 10 is converted into a reference area within each of the plots 12 in the digital map of the field 10, resulting in a map 20 as represented in FIG. 2.

As a significant number of the plants in the field 10 reach senescence, the plants in the field 10 are scanned using a radiometric sensor equipped with a high-precision GPS receiver. Each data point collected from the radiometric sensor, which preferably is in the form of an NDVI index, includes latitude and longitude information. The data points are correlated to the location of the plants on the map 20, including the check plots 18. The radiometric data collected from the check plots 18 is used to calibrate the sensor and the collected data from the experimental plots 12. The NDVI data for each plant is then assigned a relative maturity value based on its relationship to the NDVI data from the check plots 18. In a preferred embodiment, the relative maturity data is passed to a comprehensive database of other characteristics of the experimental varieties.

One method of moving the radiometric sensor over the field 10 is manually. A worker simply carries the sensor through the field, holding it above each plant in each plot. A more efficient way of taking the radiometric data is to mount one or more radiometric sensors on a vehicle that then travels the field 10, collecting data on the fly. In a preferred embodiment, a vehicle used for detasseling corn, such as a PDF 450G detassler (Product Design and Fabrication, Cedar Rapids, Iowa) is modified to create a sensor transport 22 (FIGS. 3 and 4) to carry a pair of radiometric sensors 24 and 26. A forward horizontal tool bar 28 of the sensor transport 22 is mounted transversely of the direction of travel of the transport 22. The sensors 24 and 26 are mounted on the tool bar 28, the vertical position of which is adjustable to position the sensors 24 and 26 the desired reading distance above the plant canopy. In the case of the GreenSeeker® RT100 sensor, the manufacturer recommends that the sensor be positioned between 32 inches and 48 inches above the plant canopy, typically about 30 inches for soybeans.

Applications of Phenotypic Data

The present invention provides methods for generating high through put phenotype data that can be used to characterize plants. This phenotypic data also can be employed in various types of plant breeding and selection including marker assisted breeding. This data can be utilized in analyzing seeds or plant tissue material for genetic characteristics that associate with the relative maturity or stay green phenotype of the individual plants or plants which are genetically related. The genetic characteristics associated with the plant evidencing the presences or absences of the phenotype can be determined by analytical methods. These methods can use markers or genomics data for the detection of chemical, allelic, polymorphic, base pair or amino acid differences.

Samples prepared from the phenotyped seeds or plant materials can be used to establish the desirable genetic attributes that are associated with the selected genotype. Once the selected genotype is identified, it can be used for plant selection thorough out the breeding or selection process. In one embodiment, the methods and devices of the present invention can be used in a breeding program to select plants or seeds having a desired trait, whether genetically modified or native trait or marker genotype associated with the phenotype detected with the high through put relative maturity or stay green data. The methods of the present invention can be used in combination with any breeding methodology and can be used to select a single generation or to select multiple generations or plants or seeds. The choice of breeding method depends on the mode of plant reproduction, the heritability of the trait(s) being improved, and the type of cultivar used (hybrid, inbred, varietal). It is further understood that this device produces phenotypic data for cultivars which can be utilized in a breeding program, in conjunction with selection on any number of other parameters such as emergence vigor, vegetative vigor, stress tolerance, disease resistance, branching, flowering, seed set, seed size, seed density, standability, and threshability etc. to make breeding selections or decisions.

In a particular embodiment, the methods of the present invention are used to determine the genetic characteristics of seeds or plants in a marker-assisted breeding program. Such methods allow for improved marker-assisted breeding programs wherein direct seed or tissue sampling can be conducted while maintaining the identity of individuals from the field. As a result, the marker-assisted breeding program results in a “high-throughput” platform wherein a subpopulation of seeds/plants having a desired trait, marker or genotype can be more effectively selected and bulked in a shorter period of time, with less field and labor resources required.

EXAMPLE 1

Two fields were planted on a farm located near Ames, Iowa. Field A was planted on May 21. Field A comprised 21 acres (1920 feet by 480 feet) and was divided into 36,864 plots as shown in FIG. 5. Check variety S08-M8 was planted in rows 1 and 2 and 49 and 50, and check varieties S15-R2, S21-N6, S25-B9 and S30-F5 were planted in rows 2-6 and 51-54, respectively. Experimental varieties were planted in the other plots. Specifically, experimental varieties A-N were planted in rows 7-20, respectively, and across range 1 (FIG. 7). Field C was planted June 17 the same year. Field C comprised 20 acres (1800 feet by 480 feet) and was divided into 34,560 plots as shown in FIG. 6. Check varieties S15-R2, S21-N6, S25-B9 and S30-F5 were planted in rows 1-4 and 49-52, respectively. Experimental varieties were planted in the other plots. Specifically, experimental varieties O-Z and A1-D1 were planted in rows 5-20, respectively, and across range 1 (FIG. 8). The check varieties covered maturity groups 0.8-3, as set out in Tables 1 and 2.

TABLE 1
Check Varieties used in Field A
#	Variety	Maturity Group
1	S08-M8	0.8
2	S15-R2	1.5
3	S21-N6	2.0
4	S25-B9	2.5
5	S30-F5	3.0

TABLE 2
Check Varieties used in Field C
#	Variety	Maturity Group
1	S15-R2	1.5
2	S21-N6	2.0
3	S25-B9	2.5
4	S30-F5	3.0

The seed was planted at a density of 10 seeds per foot and a row width 30 inches and a GPS map of the seed planted in the fields was created at the time of planting. Data was collected from Field A on September 2, and from Field C on September 23 and 26 of the same year. Data was collected using the PDF 450G detasseling machine, modified as shown in FIGS. 3 and 4. The speed of the detassler was approximately 3 mph and it was driven transverse to the rows. The GreenSeeker® RT100 sensor was set to collect data at 50 msec (20 data points per second) to match the GPS data stream from the NovAtel ProPak®-V3 device. To reduce row to row sample contamination, 15 inches of each 30 inch row was considered the target collection area. At 3 mph, 15 inches is covered in 0.28 sec, giving 5.7 data points per plot, which was deemed an acceptable number of data points per plot. The data output of the GPS and radiometric crop sensor taken from Field A is set out in Table 3.

TABLE 3
Data Output of the Crop Sensor for Plots 1-20, Field A
Time	Record	Latitude	Longitude	Plot	NDVI
12:04:53	183	−93.49205711	42.12766835	1	0.819
12:04:53	184	−93.49205654	42.12766844	1	0.846
12:04:53	185	−93.49205606	42.1276685	1	0.773
12:04:53	186	−93.49205563	42.12766852	1	0.614
12:04:53	187	−93.49205477	42.1276687	1	0.549
12:04:53	188	−93.49205425	42.12766869	1	0.622
12:04:53	189	−93.49205375	42.1276687	1	0.593
12:04:53	190	−93.49205319	42.12766871	1	0.517
12:04:53	197	−93.49204749	42.12766879	2	0.246
12:04:54	198	−93.49204689	42.12766877	2	0.267
12:04:54	199	−93.49204544	42.1276688	2	0.242
12:04:54	200	−93.4920447	42.12766891	2	0.382
12:04:54	201	−93.49204396	42.12766893	2	0.413
12:04:54	202	−93.49204334	42.12766895	2	0.502
12:04:54	208	−93.49203788	42.1276687	3	0.598
12:04:54	209	−93.49203712	42.12766865	3	0.755
12:04:54	210	−93.49203632	42.1276687	3	0.810
12:04:54	211	−93.49203466	42.12766882	3	0.757
12:04:54	212	−93.49203381	42.12766891	3	0.755
12:04:55	217	−93.49202869	42.12766931	4	0.708
12:04:55	218	−93.49202768	42.12766924	4	0.900
12:04:55	219	−93.49202592	42.1276692	4	0.824
12:04:55	220	−93.49202502	42.12766913	4	0.795
12:04:55	225	−93.49201984	42.12766903	5	0.780
12:04:55	226	−93.49201904	42.12766902	5	0.623
12:04:55	227	−93.4920181	42.1276689	5	0.912
12:04:55	228	−93.49201613	42.12766894	5	0.904
12:04:56	233	−93.49201096	42.12766923	6	0.894
12:04:56	234	−93.49200991	42.1276691	6	0.855
12:04:56	235	−93.49200799	42.12766927	6	0.807
12:04:56	236	−93.49200706	42.12766925	6	0.840
12:04:56	237	−93.49200631	42.12766935	6	0.842
12:04:56	241	−93.49200176	42.12766948	7	0.876
12:04:56	242	−93.49200122	42.12766941	7	0.822
12:04:56	243	−93.49200017	42.12766952	7	0.846
12:04:56	244	−93.49199914	42.12766941	7	0.848
12:04:56	245	−93.49199817	42.12766927	7	0.892
12:04:57	246	−93.4919973	42.12766928	7	0.838
12:04:57	251	−93.49199138	42.12766927	8	0.784
12:04:57	252	−93.49199052	42.12766915	8	0.843
12:04:57	253	−93.49198978	42.12766919	8	0.761
12:04:57	254	−93.49198897	42.12766926	8	0.698
12:04:57	260	−93.49198176	42.12766923	9	0.852
12:04:57	261	−93.49198066	42.12766927	9	0.821
12:04:58	262	−93.49197951	42.12766931	9	0.835
12:04:58	263	−93.49197741	42.12766932	9	0.829
12:04:58	268	−93.49197159	42.1276692	10	0.881
12:04:58	269	−93.49197042	42.12766912	10	0.875
12:04:58	270	−93.49196928	42.12766912	10	0.902
12:04:58	275	−93.49196294	42.12766905	11	0.730
12:04:58	276	−93.49196213	42.1276691	11	0.526
12:04:58	277	−93.49196133	42.12766906	11	0.494
12:04:59	278	−93.4919606	42.12766907	11	0.473
12:04:59	279	−93.49195872	42.12766902	11	0.548
12:04:59	284	−93.49195357	42.12766911	12	0.719
12:04:59	285	−93.49195255	42.12766917	12	0.669
12:04:59	286	−93.49195171	42.12766907	12	0.599
12:04:59	287	−93.49195044	42.12766914	12	0.733
12:04:59	288	−93.49194964	42.12766905	12	0.735
12:04:59	289	−93.49194964	42.12766905	12	0.719
12:04:59	292	−93.4919446	42.12766914	13	0.800
12:04:59	293	−93.49194387	42.12766917	13	0.871
12:05:00	294	−93.49194323	42.12766925	13	0.767
12:05:00	295	−93.4919418	42.12766933	13	0.851
12:05:00	296	−93.4919408	42.12766935	13	0.827
12:05:00	301	−93.49193573	42.12766943	14	0.822
12:05:00	302	−93.49193489	42.12766941	14	0.799
12:05:00	303	−93.49193283	42.12766921	14	0.873
12:05:00	304	−93.49193176	42.12766912	14	0.875
12:05:01	310	−93.4919259	42.12766878	15	0.799
12:05:01	311	−93.49192359	42.1276688	15	0.870
12:05:01	312	−93.49192256	42.1276688	15	0.868
12:05:01	318	−93.49191677	42.12766888	16	0.786
12:05:01	319	−93.49191486	42.12766886	16	0.799
12:05:01	320	−93.49191381	42.12766882	16	0.762
12:05:01	321	−93.49191295	42.12766883	16	0.765
12:05:02	327	−93.49190737	42.12766875	17	0.740
12:05:02	328	−93.49190556	42.12766879	17	0.771
12:05:02	329	−93.49190475	42.12766875	17	0.721
12:05:02	330	−93.49190392	42.12766887	17	0.792
12:05:02	335	−93.49189769	42.1276689	18	0.772
12:05:02	336	−93.49189683	42.12766892	18	0.809
12:05:02	337	−93.49189599	42.1276688	18	0.865
12:05:02	338	−93.49189517	42.127669	18	0.845
12:05:03	343	−93.49188915	42.12766882	19	0.682
12:05:03	344	−93.49188824	42.12766885	19	0.724
12:05:03	345	−93.49188738	42.12766884	19	0.833
12:05:03	346	−93.49188647	42.12766888	19	0.685
12:05:03	352	−93.49187979	42.12766905	20	0.636
12:05:03	353	−93.49187893	42.12766902	20	0.531
12:05:03	354	−93.49187795	42.127669	20	0.555
12:05:03	355	−93.4918763	42.12766887	20	0.399

The average NDVI for the check and experimental varieties is set out in Table 4.

TABLE 4
Average NDVI for Each Variety in Plots 1-20, Field A
        Avg.
Variety NDVI
S08-M8  0.667
S08-M8  0.342
S15-R2  0.735
S21-N6  0.807
S25-B9  0.805
S30-F5  0.848
A       0.854
B       0.772
C       0.834
D       0.886
E       0.554
F       0.696
G       0.823
H       0.842
I       0.846
J       0.778
K       0.756
L       0.823
M       0.731
N       0.530

The data output of the GPS and radiometric crop sensor taken from Field C is set out in Table 5.

TABLE 5
Data Output of the Crop Sensor for Plots 1-20, Field C
Time	Record	Longitude	Latitude	Row	NDVI1
11:58:31	305	−93.4921	42.12471	1	0.230
11:58:31	306	−93.4921	42.12471	1	0.208
11:58:31	307	−93.4921	42.12471	1	0.252
11:58:31	308	−93.4921	42.12471	1	0.245
11:58:31	309	−93.4921	42.12471	1	0.195
11:58:31	310	−93.4921	42.12471	1	0.262
11:58:31	311	−93.4921	42.12471	1	0.287
11:58:31	312	−93.4921	42.12471	1	0.219
11:58:32	318	−93.4921	42.12471	2	0.244
11:58:32	319	−93.4921	42.12471	2	0.219
11:58:32	320	−93.4921	42.12471	2	0.209
11:58:32	321	−93.4921	42.12471	2	0.253
11:58:32	322	−93.4921	42.12471	2	0.253
11:58:32	323	−93.4921	42.12471	2	0.297
11:58:32	330	−93.4921	42.12471	3	0.307
11:58:33	331	−93.492	42.12471	3	0.425
11:58:33	332	−93.492	42.12471	3	0.276
11:58:33	333	−93.492	42.12471	3	0.270
11:58:33	334	−93.492	42.12471	3	0.293
11:58:33	335	−93.492	42.12471	3	0.383
11:58:33	342	−93.492	42.12471	4	0.268
11:58:33	343	−93.492	42.12471	4	0.381
11:58:33	344	−93.492	42.12471	4	0.744
11:58:33	345	−93.492	42.12471	4	0.778
11:58:33	346	−93.492	42.12471	4	0.800
11:58:34	352	−93.492	42.12471	5	0.726
11:58:34	353	−93.492	42.12471	5	0.738
11:58:34	354	−93.492	42.12471	5	0.725
11:58:34	355	−93.492	42.12471	5	0.777
11:58:34	356	−93.492	42.12471	5	0.485
11:58:34	357	−93.492	42.12471	5	0.556
11:58:35	364	−93.492	42.12471	6	0.479
11:58:35	365	−93.492	42.12471	6	0.395
11:58:35	366	−93.492	42.12471	6	0.383
11:58:35	367	−93.492	42.12471	6	0.729
11:58:35	368	−93.492	42.12471	6	0.813
11:58:35	369	−93.492	42.12471	6	0.715
11:58:35	376	−93.492	42.12471	7	0.742
11:58:35	377	−93.492	42.12471	7	0.506
11:58:35	378	−93.492	42.12471	7	0.344
11:58:36	379	−93.492	42.12471	7	0.170
11:58:36	380	−93.492	42.12471	7	0.255
11:58:36	381	−93.492	42.12471	7	0.208
11:58:36	388	−93.492	42.12471	8	0.157
11:58:36	389	−93.492	42.12471	8	0.216
11:58:36	390	−93.492	42.12471	8	0.222
11:58:36	391	−93.492	42.12471	8	0.217
11:58:36	392	−93.492	42.12471	8	0.293
11:58:36	393	−93.492	42.12471	8	0.215
11:58:36	394	−93.492	42.12471	9	0.270
11:58:37	395	−93.492	42.12471	9	0.253
11:58:37	396	−93.492	42.12471	9	0.294
11:58:37	397	−93.492	42.12471	9	0.241
11:58:37	398	−93.492	42.12471	9	0.214
11:58:37	399	−93.492	42.12471	9	0.184
11:58:37	400	−93.492	42.12471	9	0.294
11:58:37	406	−93.492	42.12471	10	0.256
11:58:37	407	−93.492	42.12471	10	0.263
11:58:37	408	−93.492	42.12471	10	0.322
11:58:37	409	−93.492	42.12471	10	0.268
11:58:37	410	−93.492	42.12471	10	0.270
11:58:38	411	−93.492	42.12471	10	0.325
11:58:38	417	−93.492	42.12471	11	0.284
11:58:38	418	−93.492	42.12471	11	0.330
11:58:38	419	−93.492	42.12471	11	0.270
11:58:38	420	−93.492	42.12471	11	0.340
11:58:38	421	−93.492	42.12471	11	0.437
11:58:38	422	−93.492	42.12471	11	0.420
11:58:39	427	−93.492	42.12471	12	0.489
11:58:39	428	−93.492	42.12471	12	0.426
11:58:39	429	−93.492	42.12471	12	0.341
11:58:39	430	−93.492	42.12471	12	0.263
11:58:39	431	−93.492	42.12471	12	0.218
11:58:39	436	−93.492	42.12471	13	0.247
11:58:39	437	−93.492	42.12471	13	0.199
11:58:39	438	−93.492	42.12471	13	0.246
11:58:39	439	−93.492	42.12471	13	0.248
11:58:39	440	−93.492	42.12471	13	0.196
11:58:39	441	−93.492	42.12471	13	0.250
11:58:40	446	−93.492	42.12471	14	0.235
11:58:40	447	−93.492	42.12471	14	0.292
11:58:40	448	−93.492	42.12471	14	0.256
11:58:40	449	−93.492	42.12471	14	0.263
11:58:40	450	−93.492	42.12471	14	0.216
11:58:40	451	−93.4919	42.12471	14	0.287
11:58:40	457	−93.4919	42.12471	15	0.358
11:58:40	458	−93.4919	42.12471	15	0.334
11:58:41	459	−93.4919	42.12471	15	0.331
11:58:41	460	−93.4919	42.12471	15	0.331
11:58:41	461	−93.4919	42.12471	15	0.268
11:58:41	467	−93.4919	42.12471	16	0.372
11:58:41	468	−93.4919	42.12471	16	0.264
11:58:41	469	−93.4919	42.12471	16	0.250
11:58:41	470	−93.4919	42.12471	16	0.247
11:58:41	471	−93.4919	42.12471	16	0.268
11:58:41	472	−93.4919	42.12471	16	0.195
11:58:42	480	−93.4919	42.12471	17	0.225
11:58:42	481	−93.4919	42.12471	17	0.238
11:58:42	482	−93.4919	42.12471	17	0.232
11:58:42	483	−93.4919	42.12471	17	0.204
11:58:42	484	−93.4919	42.12471	17	0.200
11:58:42	485	−93.4919	42.12471	17	0.199
11:58:43	491	−93.4919	42.12471	18	0.198
11:58:43	492	−93.4919	42.12471	18	0.198
11:58:43	493	−93.4919	42.12471	18	0.176
11:58:43	494	−93.4919	42.12471	18	0.182
11:58:43	495	−93.4919	42.12471	18	0.287
11:58:43	501	−93.4919	42.12471	19	0.325
11:58:43	502	−93.4919	42.12471	19	0.198
11:58:43	503	−93.4919	42.12471	19	0.298
11:58:43	504	−93.4919	42.12471	19	0.298
11:58:43	505	−93.4919	42.12471	19	0.259
11:58:43	506	−93.4919	42.12471	19	0.244
11:58:44	511	−93.4919	42.12471	20	0.135
11:58:44	512	−93.4919	42.12471	20	0.186
11:58:44	513	−93.4919	42.12471	20	0.220
11:58:44	514	−93.4919	42.12471	20	0.331
11:58:44	515	−93.4919	42.12471	20	0.332

The average NDVI for the check and experimental varieties is set out in Table 6.

TABLE 6
Average NDVI for Each Variety in Plots 1-20, Field C
        Avg.
Variety NDVI
S15-R2  0.237
S21-N6  0.246
S25-B9  0.326
S30-F5  0.594
O       0.668
P       0.586
Q       0.371
R       0.220
S       0.250
T       0.284
U       0.347
V       0.347
W       0.231
X       0.258
Y       0.324
Z       0.266
A1      0.216
B1      0.208
C1      0.270
D1      0.241

The NDVI data is correlated to maturity groups by a graph of relative maturity value (RMT_N), determined by the average NDVI for each check variety, versus NDVI. The graph from Field A is shown in FIG. 9 and the graph from Field C is shown in FIG. 10. Personal observation indicated the varieties mature at scanning were the maturity group late 1.0 in Field A and maturity group 1.7 in Field C. The data shows the final average maturity of field A was a maturity of 2.2 and field C was 2.0.

EXAMPLE 2

Experiment Corn Staygreen Phenotyping Methodology Trial

An experiment using the devices shown in FIGS. 11a and 11b were employed on maize to detect the staygreen of plants in trials. Staygreen is a function of plant health, plant stress, insect and disease pressures on the plant These stay green trials were maize inbred trials and maize hybrids trials. The hybrid trials had 8, 30 inch rows, 40 foot long plots. The data was collected with canopy readings taken between rows four and five, of all 65 plots. Below canopy readings taken between rows four and five, on the first set of 16 plots.

The inbred trial had 1, 30 inch row, 20 foot long plots. The above canopy readings taken over the row, for the first 100 plots. In all the trials, five readings, one per week, were taken. Some frost damage occurred between the 4^th reading and last collection date. Average staygreen readings were taken as visual readings and active sensor readings as shown in FIGS. 12a and 12b.

In the inbred trial a 50% visual staygreen was a good indicator for defining the center of three week stable scanning period.

The graphs in FIGS. 13a-g depict the correlation of the staygreen visual and active sensor readings across time. The 60% for hybrids and the 50% staygreen for inbreds is a good indicator for peak correlation of visual phenotype detection with the active sensor readings. In this experiment at peak data collection date the sensor was employed to identify nine of the top ten staygreen hybrids ranked by visual selection.

This phenotypic data can be used in a trait mapping experiment to develop genetic characteristics that associate with the phenotype of staygreen. This high through put automated data collection can be utilized in indentifying markers that associate with staygreen phenotypes. The phenotypic data can then be employed in the development of a marker assisted breeding programs. This data can also be captured across time to identify the most critical time for silage production or the prime harvesting timeframes for inbreds or hybrids. This data can be sent from the device to a remote location for analysis of the data. The method of the present invention include capturing the sensor data and analyzing the data for use in phenotyping, marker validation and selection, marker assisted breeding, selections, and producing breeding programs with inbreds and hybrid combination and the seeds and plants and progeny thereof that have the phenotypic traits introgressed through use of the breeding material mapped or selected for relative maturity, staygreen, health, disease, stress, vigor and the like.

EXAMPLE 3

Sudden Death Syndrome

The seed was planted at a density of 10 seeds per foot and a row width 30 inches and a GPS map of the seed planted in the fields was created at the time of planting. Data was to be collected from the soybean field for determination of relative maturity. However, prior to the time period for data collection the field was highly impacted by Sudden Death Syndrome (SDS). This disease causes plants particularly those in the R4-R6 stage to die prematurely. Premature death of part of the plants in the field most susceptible to Sudden Death Syndrome would skew any relative maturity ratings. It was determined that data will be collected using the PDF 450G detasseling machine, modified as shown in FIGS. 3 and 4. The speed of the detassler will be approximately 3 mph and driven transverse to the rows. The GreenSeeker® RT100 sensor will initially be set to collect data at 50 msec (20 data points per second) to match the GPS data stream from the NovAtel ProPak®-V3 device. To reduce row to row sample contamination, 15 inches of each 30 inch row will be considered the target collection area. At 3 mph, 15 inches is covered in 0.28 sec, giving 5.7 data points per plot, which is deemed an acceptable number of data points per plot. However, by employing two sensors per row at a slightly lower rate, 63 Hz, 4.5 data points per second from 2 sensors provides 9 data points per plot. Instead of collecting data to determine the relative maturity of the plants, the data is being collected to determine which plants in the plots are susceptible to SDS and which are more tolerant. Because SDS does not impact all areas of a field evenly some susceptible plants in less impacted areas may not be identified as susceptible, but due to the intense pressure of SDS in the fields most susceptible plots will be readily identified. The data output of the GPS and radiometric crop sensor should allow selection of plants in plots that are less susceptible to the fungal disease SDS. These plants can then be used in further breeding, selection, and development programs including marker assisted breeding development programs.

The foregoing description comprises illustrative embodiments of the present inventions. The foregoing embodiments and the methods described herein may vary based on the ability, experience, and preference of those skilled in the art. Merely listing the steps of the method in a certain order does not necessarily constitute any limitation on the order of the steps of the method. The foregoing description and drawings merely explain and illustrate the invention, and the invention is not limited thereto, except insofar as the claims are so limited. Those skilled in the art who have the disclosure before them will be able to make modifications and variations therein without departing from the scope of the invention

Claims

1. (canceled)

2. (canceled)

3. (canceled)

4. (canceled)

5. (canceled)

6. (canceled)

7. A system for phenotyping plants, the system comprising:

(a) plants comprising a plurality of micro-plots of one or more plants;

(b) a sensing apparatus;

(c) a vehicle mounting the sensing apparatus for transport of the sensing apparatus over the row of plants;

(d) a data signal generated by the sensing apparatus corresponding to the evidence selected from the group consisting of relative maturity, staygreen, health, vigor, disease, stress, biomass of the plant or plants in the single micro-plot; and

(f) a computer for receiving and storing the data signal associated with each micro-plot.

8. A system of claim 7 further comprising at least one device for determination of GPS.

9. A system of claim 7 wherein said micro-plots can include one or more plots.

10. A method of plant breeding, comprising the steps of

(a) growing a plot of the plants through to a selected maturity stage;

(b) collecting radiometric sensor data from the plot corresponding to relative maturity, staygreen, health, vigor, disease, stress, biomass of the plants in the plot;

(c) analyzing the sensor data to generate a measure of relative maturity, staygreen, health, vigor, disease, stress, biomass of plants in a plot; and

(d) using the measure of one or more of the relative maturity, staygreen, health, vigor, disease, stress, biomass as a basis for selecting between plants in plant breeding.

11. The method of claim 10, wherein a GPS is used to record the location of seed planted with the plots.

12. The method of claim 11, wherein GPS is used to correlate the sensor data to the location of seeds planted in the plots.

13. The method of claim 10, wherein plants of varieties of at least one known relative maturity, staygreen, health, vigor, disease, stress, biomass are included in the plots as check plants.

14. The system of claim 7, wherein the sensing apparatus is mounted on a vehicle and supported above the plants.

15. A method for measuring at least one characteristic of a plurality of plots of diverse varieties of plants growing in a field, comprising the steps of (a) growing a plot and recording the position of the variety planted in each plot;

(b) collecting radiometric sensor data from each plot corresponding to at least one selected characteristic of plants in the plot, and

(c) analyzing the sensor data to generate a measure of the characteristic of the plants in a plot.

16. A method according to claim 15 wherein said characteristic is relative maturity.

17. A method according to claim 15 wherein said characteristic is staygreen.

18. A method according to claim 15 wherein said characteristic is health.

19. A method according to claim 15 wherein said characteristic is vigor.

20. A method according to claim 15 wherein said characteristic is disease.

21. A method according to claim 15 wherein said characteristic is stress.

22. A method according to claim 15 wherein said characteristic is biomass.