Qamar Uz Zaman

VARIABLE RATE NITROGEN APPLICATION IN FLORIDA CITRUS BASED ON ULTRASONICALLY-SENSED TREE SIZE

Most Florida citrus groves are still managed as large contiguous uniform blocks, despite significant variation in fruit yield and tree canopy size. Site-specific grove management by variable rate delivery of inputs such as fertilizers on a tree size basis could improve horticultural profitability and environmental protection. Tree canopy sizes were measured real-time in a typical 17-ha Valencia grove with an automated ultrasonic sensor system equipped with Differential Global Positioning System (DGPS). Prescription maps for variable application of nitrogen fertilizer were generated from ultrasonically scanned tree sizes on a single tree basis using ArcView GIS and Midtech Fieldware. Leaf samples from trees with different canopy sizes, which had been fertilized at a conventional uniform rate of 270 kg N/ha/y, were analyzed for nitrogen concentration. Analysis of 2980 tree spaces in the grove showed a skewed size distribution, with 62% in the 0- to 100-m3/tree volume classes and a median volume of 79 m3/tree. The tree volumes ranged from 0 to 240 m3/tree. Regression analysis showed that trees with excess leaf nitrogen (>3%) had canopies less than 100 m3. These trees receiving excess nitrogen are likely to have lower fruit yields and quality, and wasted fertilizer nitrates may leach beyond the root zone to groundwater. In order to rectify the excess fertilization of smaller trees, a granular fertilizer spreader with hydraulically powered split-chain outputs controlled with a MidTech Legacy 6000 controller was used for variable rate application of nitrogen in one-half of the grove. A 38% to 40% saving in granular fertilizer cost was achieved for this grove when variable N rates were implemented on a per-tree basis ranging from 135 to 270 kg N/ha/y.

Estimation of Citrus Fruit Yield Using Ultrasonically-Sensed Tree Size

Tree canopy mapping with an automated ultrasonic system is inexpensive, fairly straightforward and could be used to estimate fruit yield within a grove to plan site-specific management practices. Tree canopy volumes and fruit yield were measured and mapped in a 17.0-ha ‘Valencia’ grove with an automated ultrasonic system and a sensor-based automatic yield monitoring system, respectively. The spatial data were divided into 40 equal sized plots using ArcView GIS software to relate tree sizes and fruit yield. A linear calibration model with half the data showed that ultrasonically-sensed tree sizes correlated significantly (R2 = 0.80) with fruit yield. The correlation between actual and predicted fruit yield with the remaining data was used for validation and was also significant (R2 = 0.42). The average prediction accuracy was 90.6% while the standard error of prediction and root mean square error were 4.25 and 4.16 Mg/ha, respectively. The ultrasonically-sensed tree canopy volumes of plots ranged from 7421 to 20,900 m3/ha;fruit yield within the grove was also variable, ranging from 21 to 45 Mg/ha. Tree size and yield maps produced similar spatial patterns within the grove, as high-yielding areas were associated with large tree canopies. Therefore, tree canopy size could be used to estimate fruit yield within the grove. This information is valuable to forecast yields, to plan harvest schedules and to generate prescription maps for site-specific management practices on an individual tree basis.

Estimation of Wild Blueberry Fruit Yield Using Digital Color Photography

The wild blueberry industry of North America may benefit significantly from precision agriculture technology. Currently, crop management practices are implemented on an average basis without considering the substantial variation in soil properties, bare spots, topographic features, and fruit yield in blueberry fields. Yield maps along with fertility, weed, and topographic maps can be used to generate prescription maps for site-specific application of agrochemicals (e.g., fertilizer or pesticide). Two wild blueberry fields in central Nova Scotia were selected to evaluate a photographic method for fruit yield estimation. A 10-megapixel 24-bit digital color camera was mounted on a tripod and pointed downwards to take photographs of the blueberry crop from a height of approximately 1 m. At harvest time, blueberry crop images were collected in each field at 30 different sample locations displaying a range in yield. Actual fruit yield was sampled from the same locations by hand-harvesting out of a 0.5 × 0.5 m quadrat using a commercial blueberry rake. Custom image processing software was developed to count the blue pixels of ripe fruit in the quadrat region of each image and express it as a percentage of total quadrat pixels. Linear regression was used to calibrate the fruit yield with percentage blue pixels separately in each field, and then the calibration equation of field 1 was used to predict fruit yield in field 2 for validation of the method. Percentage blue pixels correlated highly significantly with hand-harvested fruit yield in field 1 (R2 = 0.98; P < 0.001; n = 30) and field 2 (R2 = 0.99; P < 0.001; n = 30). The correlation between actual and predicted fruit yield in field 2 (validation) was also highly significant (R2 = 0.99; P < 0.001; n = 30; RMSE = 277 kg/ha). Non-significance of the t-test for actual versus predicted yield indicated that there was no significant bias in the yield estimation and that the predicted yield was accurate. Based on these results, an automated yield monitoring system consisting of a digital camera, computer, and DGPS will be developed and incorporated into a harvester to monitor and map blueberry fruit yield in real time.

VARIABLE RATE GRANULAR FERTILIZATION OF CITRUS GROVES: SPREADER PERFORMANCE WITH SINGLE-TREE PRESCRIPTION ZONES

Commercial variable rate technology (VRT) fertilizer spreaders for citrus are currently being implemented in Florida groves to improve profitability and reduce nitrate contamination of groundwater. Although VRT spreaders incorporate proven embedded controllers and tree sensors which permit changing fertilizer rates according to tree size, there is currently limited information on their performance characteristics in spatially variable groves. This study investigated the performance characteristics of a split-chain, spinner-type VRT spreader during fertilization of a commercial citrus grove. Six nitrogen rates (0, 134, 168, 202, 236, and 270 kg ha-1 y-1) were varied according to a prescription map developed from ultrasonically measured tree size information. Missing trees and one-year-old reset trees were not fertilized with the spreader. Target fertilizer rates for 1490 trees in an 8.1-ha experimental area were compared with actual fertilizer rates calculated from geartooth speed sensors monitoring the conveyor chain speed. Through classification and regression analysis, spreader performance and response times during transitions from zero or low fertilizer rates to high rates and vice versa were compared. In this grove, 73.1% of the fertilizer rate changes were required between a single-tree space of 5.3-m linear row distance, taking about 4 s to drive at 1.34 m s-1. The spreader had an average on-off response time of . 3 s, and an average rate changing response time of 2 to 5 s. Based on these data, the spreader design is not suitable for rapid fertilizer rate changes between single tree spaces, but could be greatly improved by substituting its hydraulic servo control valves with faster devices.

Automated, Low-Cost Yield Mapping of Wild Blueberry Fruit

The presence of weeds, bare spots, and variation in fruit yield within wild blueberry fields emphasizes the need for yield mapping for site-specific application of agrochemicals. An automated yield monitoring system (AYMS) consisting of a digital color camera, differential global positioning system, custom software, and a ruggedized laptop computer was developed and mounted on a specially designed Farm Motorized Vehicle (FMV) for real-time fruit yield mapping. Two wild blueberry fields were selected in central Nova Scotia to evaluate the performance of the AYMS. Calibration was carried out at 38 randomly selected data points, 19 in each field. The ripe fruit was hand-harvested out of a 0.5- × 0.5-m quadrant at each selected point and camera images were also taken from the same points to calculate the blue pixel ratio (fraction of blue pixels in the image). Linear regression was used to calibrate the actual fruit yield with percentage blue pixels. Real-time yield mapping was carried out with AYMS. Custom software was developed to acquire and process the images in real-time, and store the blue pixel ratio. The estimated yield per image along with geo-referenced coordinates was imported into ArcView 3.2 GIS software for mapping.

MAPPING WATER TABLE DEPTH BY ELECTROMAGNETIC INDUCTION

Currently most citrus groves are managed uniformly in large blocks, despite significant spatial variability in soil and water properties that influences the long-term viability of perennial citrus trees in Florida’s poorly drained Southern Flatwood soils. Detailed georeferenced water table maps would be useful to select suitable land for new citrus grove development, for drainage system design, or to identify and manage drainage problems in existing groves. The objective of this study was to develop a new precision agriculture application using ground conductivity measured with the EM38 electromagnetic soil profiler for the estimation and mapping of shallow water table depths in Florida’s citrus groves. Calibrations were developed and tested in five different months and two sites to evaluate the spatial and temporal accuracy of water table predictions. Three automated mobile surveys of water table depth were then conducted in the same groves using a differential global positioning system (DGPS) for georeferencing the ground conductivity data. The spatial variability of water table depths was mainly determined by soil type and the temporal variability was influenced strongly by the seasonal rainfall pattern. The vertical dipole (EMv) of the EM38 instrument was better than the horizontal dipole (EMh) for estimating water table depths because of its greater sensing depth. Accuracy, calculated as root mean square error (RMSE), ranged from 4.1 to 15.5 cm on a given day. Significant bias was however evident when comparing calibrations developed on different sites which were 12-km apart. Global calibrations incorporating a rainfall index as well as EMv were much better predictors of water table depth due to the added temporal information. The best results were obtained by using site-and-time-specific calibrations of <10 points for every survey, using a representative range of EMv values.

Detecting Bare Spots in Wild Blueberry Fields Using Digital Color Photography

Wild blueberry fields are developed from native stands on deforested land by removing competing vegetation. The majority of fields are situated in naturally acidic and non-fertile soils that have high proportions of bare spots, weed patches, and gentle to severe topography. Producers presently apply agrochemicals uniformly without considering bare spots. The unnecessary or over-application of agrochemicals in bare spots may increase cost of production and environmental pollution. An automated cost-effective machine vision system using digital color photography was developed and tested to detect and map bare spots for site-specific application of agrochemicals within wild blueberry fields. The experiment was conducted at a 4-ha wild blueberry field in central Nova Scotia. The machine vision system consisting of a digital color camera, differential global positioning system, and notebook computer was mounted on a specialized farm vehicle. Custom software for grabbing and processing color images was developed in Delphi 5.0 and C++ programming languages. The images taken by the digital camera were stored in the notebook computer automatically and then processed in red, green, and blue (RGB), and hue, saturation, and value (HSV) color spaces to detect bare spots in real-time within blueberry fields. The best results were achieved in hue image color space with 99% accuracy and a processing speed of 661 ms per image. The results indicated that bare spots could be identified and mapped with this cost-effective digital photography technique in wild blueberry fields. This information is useful for site-specific application, and has the potential to reduce agrochemical usage and associated environmental impacts in the wild blueberry production system.

Development of Color Co-occurrence Matrix Based Machine Vision Algorithms for Wild Blueberry Fields

Co-occurrence matrix-based textural features were analyzed and three algorithms were developed to identify bare spots, wild blueberry plants, and weeds with the aim of applying agrochemicals to wild blueberry cropping fields in a spot-specific manner. Images were acquired using four cameras and a ruggedized laptop with custom-written programs coded in Microsoft Visual® C++. Textural features were extracted from the images using MATLAB® and analyzed with SAS®. Forty-four textural features were extracted from co-occurrence matrices of NTSC luminance (L), hue, saturation, and intensity (HSI) images. Multiple discriminant analysis using all 44 features (DF_ALL model) showed 98.1% of overall classification accuracy and 83 ms of processing time of an image with C++ calculation. Based on the results of multiple discriminant analysis and two-step linear discrimination plotting, the DF_HSISD, DF_SISD, and HSILD algorithms are preferred algorithms with overall accuracy of 94.9%, 92.7%, and 91.4%, and processing time of 55, 27, and 29 ms, respectively. Any of three reduced textural feature algorithms can be employed for spot-specific application of agrochemicals in wild blueberry cropping fields. The choice of one algorithm over another will depend on whether processing speed or accuracy is more important for the end-user’s application.

Response of Wild Blueberry Yield to Spatial Variability of Soil Properties

Abstract Crop management practices within wild blueberry fields are implemented uniformly, with inadequate attention being given to substantial variations in soil properties and fruit yield. These variations emphasize the need for precise and site-specific crop management based on proper characterization and quantification of spatial soil variability. The objective of this research was to characterize and quantify the spatial patterns of variability in soil properties and fruit yield. A grid pattern of sampling points was established at each experimental site to collect soil and fruit yield samples during 2009 to 2010 at North River and Carmal sites, Nova Scotia, Canada. The soil samples were collected from 0- to 15-cm depth at each grid point. These soil samples were analyzed for soil organic matter, pH, texture, electrical conductivity, and inorganic nitrogen (N). The volumetric moisture content (&thetas;v) was recorded at each grid point using time domain reflectometry. Ground conductivity readings were also recorded using dual EM conductivity meter at same selected grid points. Fruit yield was mapped using calibrated digital color photography during the crop year. The coefficient of variation of the soil properties and fruit yield suggested moderate to high variability (coefficient of variation >15%) except for soil pH. The results of correlation analysis indicated that the values for inorganic N, soil organic matter, electrical conductivity, clay, and &thetas;v were generally higher in high-yielding areas and vice versa. The trend was opposite for sand and silt content, which were higher in low-yielding areas. Most of the soil properties had a large spatial variation with semivariogram range of 20 to 50 m and varied greatly within field. Kriged maps of soil properties and fruit yield also showed substantial variation within field. Characterization of spatial soil variability in wild blueberry fields would assist in planning future soil sampling in new fields showing soil and/or yield variability. The selection of soil sampling should be based on the sampling interval suggested by the variogram range (<20 m). Proper soil sampling can help in identifying yield-limiting soil properties and develop prescription maps for site-specific nutrient management to ameliorate unproductive areas and reduce environmental contamination.

Quantifying Sources of Error in Ultrasonic Measurements of Citrus Orchards

Ultrasonic sensors can be used to estimate tree canopy volume variability within orchards, which is useful for planning site-specific management practices and estimating crop yield. The objective of this study was to investigate the errors in tree canopy volume measured with a 10-transducer ultrasonic orchard measurement array and Trimble AgGPS 132 DGPS. Sensitivity analysis was used to investigate the magnitude of individual errors in ultrasonically-sensed tree canopy volume measurement (eUCV) caused by several factors including ground speed accuracy measured by DGPS, uncalibrated air temperature, ultrasonic transducers, and deviation in driving path from the centerline between two rows. The height error in the transducer array due to improper tire inflation and uneven ground was also estimated. Canopy volume of a selected tree measured with the ultrasonic system was used as the basis to simulate eUCV caused by each error factor. One hundred data points were simulated within the selected range of each factor to calculate eUCV and the ranges were determined on the basis of measured data and literature. The overall ranking of error sources affecting canopy volume were, from high to low 1) DGPS ground speed (±6.78%), 2) air temperature (+4.83% to -4.69% for the temperature range 5°C to 45°C), 3) ultrasonic transducer performance (±2.29%), and 4) deviations in driving path (±1.56%). The height error due to uneven ground and wheel tracks ranged from 0.025 to 0.12 m. These results could be used to control error in ultrasonically-sensed canopy volumes within orchards.