History

PHASE I

Background

Northeastern North America is the world’s leading producer of wild blueberries with over 86,000 ha under management, producing 112 million kg of fruit valued at $470 million annually. Blueberry fields are developed from native stands on deforested farmland by removing competing vegetation. The majority of fields are situated in naturally acidic soils that are low in nutrients, have high proportions of bare spots and weed patches, and on gentle to severe topography. Traditionally, growers apply agrochemicals uniformly without considering bare spots, weed patches and the substantial variability in soil/crop characteristics that exist within fields. Uniform application of chemicals therefore results in either over- or under-application. Wild blueberries are low input systems with a narrow optimal range of plant nutrients. Detrimental effects on excess N occur when too much N is applied (i.e. lowers floral bud numbers and harvestable yields). Unnecessary or over-fertilization in both bare spots and weed patches can deteriorate water quality, promote increased weed growth and reduce profit. Under-fertilization restricts yield and reduces berry quality. The unique features of the wild blueberries cropping system as compared to other closed canopy crops emphasize the need for the development of affordable, reliable and efficient automated precision agriculture systems for accurate applications to maximize profit and minimize environmental impacts.

Use of agrochemicals

Wild blueberry yields are highly dependent on agrochemicals (herbicide, fungicides, insecticide) for adequate weed, floral blights, leaf diseases and insects control.  Growers apply herbicides during the growing season (Calisto to control goldenrod, Kerb in the fall to control fescue grasses and sheep sorrel), fungicides for floral blights (Monilinia and Botrytis) and leaf diseases (Septoria and Rust), and insecticides for fruit fly. Traditionally, these agrochemicals are applied uniformly without considering significant bare spots, and weed patches that exist within fields. The repeated and excessive use of agrochemicals has resulted in increased cost of production. The over use of agrochemicals is also dangerous for the environment, for humans, for the native pollinators, and for the plants. Chemically-polluted runoff from fields cause contaminated surface and ground waters. Management of wild blueberry cropping systems with reduced agrochemical inputs that have increased in diversity and is expressing increased incidence of agrochemical resistance will require: (i) increased agrochemical use efficiency; and (ii) replacement of conventional technology with new real-time VR technology for site- specific application of agrochemicals. There is an urgent need to develop affordable, reliable real-time VR applicators, using cheap sensors/cameras and controller for spot-specific application of agrochemicals in wild blueberry cropping system.

Industry collaboration with researchers

Wild blueberry growers enjoy high demand for their crop but also face high risks managing a product that essentially grows on uncultivated, challenging terrain. The precision agriculture research chair evolved from meetings almost a decade ago when John Bragg, President and CEO of Oxford Frozen Foods Limited met with David Percival (lead investigator and plant physiologist at the DAL-AC), prioritized research and set the goal of reducing crop protection usage in both wild blueberries and carrot production by 30 per cent. Combined with this was the desire to maintain or improve pest control and also increase yield. As Dr. Percival recalls, the idea was to maintain a competitive edge and sustainable, inheritable production systems while cutting costs. Producers realized that a minimalist approach was needed to retain market share and keep building product reputation. To achieve these objectives, Oxford Frozen Foods Limited and the Nova Scotia Agricultural College (NSAC) started the process of establishing a precision agriculture research program (PARP) in 2002. Combined with the support of existing researchers and the wild blueberry producers associations in the Maritimes, the initial vision of the research was to substantially reduce crop protection usage, improve land stewardship and increase berry yields. This research was initiated in 2002 with the following objectives:

• Testing and evaluation of cost-effective sensors and controllers for the development of affordable and viable precision agriculture system to maximize farm profitability and minimize environmental impacts.
• Development of an automated cost-effective yield monitoring system using digital photography technique to map wild blueberry fruit yield in commercial fields.
• Development of a cost-effective automated slope sensing system to map topographic features in wild blueberry field.
• Evaluation of soil sensing systems to quantify and map soil variation for the development of variable rate technologies in wild blueberry cropping system.
• Determine the feasibility of using DGPS/GIS to develop prescription maps for variable rate application of agrochemicals based on the variations in soil/plant characteristics, topographic features and fruit yield.
• Development of prototype variable rate spreader for site-specific fertilization.
• Development of prototype commercial variable rate sprayer for spot-application of agrochemicals.
• Evaluate the impact of variable rate agrochemical applications on groundwater contamination and air quality.
• Assess the cost/benefit of the new PA systems in blueberry production systems and eventually other high value crops.

The outcome of the several meeting to achieve the above objectives was a six year, environmental technologies and precision agriculture initiative that received $4 million in funding. Several researchers from NSAC, Dalhousie University and Agriculture Agri-Food Canada worked together to achieve the above mentioned objectives. Dr. Qamar Zaman joined the PARP in December, 2006 as Precision Agriculture Research Chair.

Yield mapping

The DAL AC precision agriculture team emphasized yield mapping research as its first priority. In 2007, Digital color photography technique was tested and evaluated to map wild blueberry fruit yield. 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 for several fields at 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 quadrant 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. Percentage blue pixels correlated highly significantly with hand‐harvested fruit yield (R2 = 0.98; P < 0.001). Blueberry yield maps, along with fertility and topographic maps could be used to generate prescription maps for site-specific application of agrochemicals (Zaman et al., 2008; Transaction of ASABE Paper)

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. Based on these results, an automated yield monitoring system (AYMS) consisting of a digital color camera, DGPS, custom software, and a ruggedized laptop computer was developed and mounted on a specially designed Farm Motorized Vehicle for real‐time fruit yield mapping. Wild blueberry fields were selected in central Nova Scotia to evaluate the performance of the AYMS. 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). 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 Arc GIS 9.3 for mapping (Zaman et al., 2010; Transaction of ASABE Paper)

Slope measuring and mapping system

After the success of the AYMS, PA research team started working on developing a system for real-time mapping of slope (topographic features). The development of site-specific agriculture has increased the need for knowledge regarding within-field variability in factors such as soil/plant characteristics and topography that influence wild blueberry production. The majority of blueberry fields in eastern Canada have gentle to severe topography. An automated slope measurement and mapping system (SMMS) consisting of low-cost accelerometers used as tilt sensors, DGPS and laptop, and custom software was developed under the supervision of Dr. Qamar Zaman. The SMMS was mounted on an all-terrain vehicle for real-time slope measurement and mapping. Six commercial wild blueberry fields were surveyed in central Nova Scotia to evaluate the performance of SMMS. The automatically sensed slopes (SS) were also compared with manually measured slopes (MS) at randomly selected points to examine the accuracy of SMMS. The SMMS measured slope reliably in the selected fields with root mean square error ranging from 0.12 to 0.56 degrees and correlations of SS with MS of R2 = 0.95 to 0.99. The selected fields had substantial variation in slope (ranging from 0.8 to 31.0 degrees). Therefore, the use of low-cost and reliable accelerometers with a DGPS is a better option than expensive real-time kinematic DGPS for developing cost-effective SMMS to quantify and map slopes (real-time) for planning site-specific management practices in commercial fields. The SS maps or real-time SMMS could also be used to adjust vehicle speed at particularly steep slopes (Zaman et al., 2010; Hort. Technology Paper).

Soil sensing system

After the success of SMMS, Dr. Zaman and his team started working on evaluating the electromagnetic induction sensor for mapping soil properties in wild blueberry fields. EMI methods are gaining popularity due to their non-destructive nature, rapid response and ease of integration into mobile platforms for assessment of the soil moisture content, water table depth, and salinity etc. Detailed georeferenced maps would be useful for site specific management of agricultural inputs. The ground conductivity readings were recorded with Dual EM (DualEM, Milton, Ontario, Canada) at sleeted grid points. The soil samples were also collected from the same sampling points and analyzed for selected soil parameters using standard methods. The ground conductivity readings were significantly correlated with the selected soil properties (SOM, moisture content, soil texture). The wells were also installed in wild blueberry field to estimate the water table depth using EMI. The comprehensive surveys were conducted in the selected fields to measure ground conductivity for soil properties and water table depth estimation in real-time using DualEM and a DGPS (Farooque, 2011; M. Sc Thesis).

Variable rate sprayer

The majority of wild blueberry fields have high proportions of bare spots and weed patches (30 to 50%). 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 by to detect and map bare spots for site‐specific application of agrochemicals within wild blueberry fields. The machine vision system consisting of a digital color camera, DGPS, 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 (Zhang et al., 2010 and Chang et al., 2011, Applied Engg. Agri. Paper).

The unique features of the significantly better than existing precision agriculture systems, at the lower cost. This type of VR sprayer will not use prescription maps, but will rely on sensors/digital cameras to provide real-time weed, bare spots and blueberry plant detection information which will be used to dispense correct agrochemical rates site-specifically within wild blueberry fields. Most importantly, the herbicide will not be applied where no weeds have been detected and fungicides/insecticides will be applied only where blueberry plants have been detected, thus avoiding waste and environmental contamination.

In 2009, An automated prototype VR sprayer was developed for control of 8 individual nozzles on a 6.1 m sprayer boom for in-season, site-specific application of agrochemicals on weeds. The sprayer boom was divided into 8 sections and mounted behind an all-terrain vehicle (ATV) at 76.2 cm above the ground. The variable-rate control system consisted of 8 ultrasonic sensors (one per spray section) mounted on a separate boom in front of the ATV, DICKEY-john Land Manager II controller and flow valve, solenoid valves and an 8-channel VR controller interfaced to a Pocket PC (PPC) using wireless Bluetooth^® radio with Windows Mobile^® compatible software. This type of VR sprayer does not use prescription maps, but relies on sensors to provide real-time weed detection information which is used to dispense correct agrochemical rates for the weeds in a specific boom section where the weeds have been detected.

All the weeds are not tall in the wild blueberry cropping system; to target the small weeds; color contrast and/or textural feature based software was developed and connected with the VR controller. In 2010, The prototype, 20-foot sprayer equipped with 2 µEye digital color cameras on each side of the front boom, which are attached by USB cables to the computer. Custom software processes the images to detect weeds, bare spots and blueberry plants in real-time. This device has user-programmable inputs such as a before-and-after buffer, time delay and ground speed corrections. In turn, these weed or plant triggering signals are transmitted to the eight-channel computerized VR controller.  In effect, the flow rate can be adjusted automatically based on the number of nozzles operating at a specific time. The corresponding solenoid sprayer valve could be activated to spray crop protection product in the specific boom section where the weeds or plants have been detected. The vision of the research was to substantially reduce agrochemical usage, improve land stewardship, protect environment and increase berry yields. (Zaman et al., 2011: Paper Computer and Electronics in Agriculture).

In fall 2010, a prototype commercial (45-foot boom) VR sprayer was developed. The 45 ft. MS 1135E sprayer boom is divided into sixteen sections and mounted behind a John Deere 6430 farm tractor. Sixteen solenoid valves and nozzles are installed on the rear boom and connected with the 8 channels computerized controllers. Eight cameras (one for every two sections) are incorporated vertically on a 45 ft front mounted boom and attached by USB cables to the computer in the tractor cabin. Two 8-channel computer controllers receive triggering signals from the custom-made image processing software. The corresponding solenoid sprayer valve is activated to spray crop protection product in the specific boom section where the weeds or plants have been detected. Computerized controllers communicate automatically to the 20-channel MidTech controller.  MidTech controller regulates the flow rate to the nozzles through a servo valve. Self leveling sensing system was introduced in front boom to adjust camera heights automatically during field operation. Besides the substantial crop protection savings, the researchers thought the environmental benefits on a crop that must maintain its wild and pristine image for markets in Japan and Europe. Preliminary results indicate cost savings of up to 80 per cent.

Variable rate spreader 

Present nutrient management recommendations are typically uniform without considering the variation in soil and plant characteristics. However, the soils are highly variable spatially and therefore, the uniform management of agricultural inputs may results in over-application in areas with high productivity and under-application in areas with low productivity. Site-specific management of nutrients using VR spreader has been acknowledged as one means of addressing this problem. The most popular approach to manage spatial variability within fields is the use of management zones (MZs), in which field that have relatively homogeneous attributes in landscape and soil condition are subdivided, and this technique can be used to direct variable rate fertilizer application. The wild blueberry field has been reported to have significant bare spots and weed patches. Over-fertilization in weed patches and bare spots with conventional methods may affect water quality, and increase production cost. Therefore there is a need to develop map/sensor based variable rate spreader for accurate site-specific fertilization to maximize profit and minimize environmental impacts. The fertilizer spreader consists of a controller, metering device, DGPS, laptop computer, and Site Mate Farm Works Software. The variable rate spreader has been tested and evaluated for performance accuracy in wild blueberry fields. Repentantly, a prescription map was generated in ArcGIS based on the slope variation. The bare spots were mapped using GPS and were defined as a separate class in the prescription map. Zero fertilizer was allocated in the bare spots. One part of the field was fertilized uniformly at grower’s rate. The prescription map was uploaded in Farm Works software to apply variable rate fertilization as prescribed in the map. Lysemeters were also installed in the experimental field. The purpose of this experiment was to visualize the impact of variable fertilization on fruit yield and ground water quality.

PHASE II

Wild blueberries follow a two-year production cycle where one year produces vegetative growth, followed by a year in which bloom, pollination, fruit growth and development occur. The stem of the wild blueberry crop typically range in height from 2 to 10 inches and the fruit size ranges from less than 3/16 inches to greater than 1/2 inches. The plant cover is less than 50% to 70% in newly developed fields. Wild blueberry fruit has the characteristic of remaining on the plant fully ripe until the greener berries reach maturity and harvesting does not take place until approximately 90% of the berries are blue. Harvesting of the wild blueberry crop in Canada begins in early August and usually lasts for a month. Berries must be harvested before frost occurs.

Over the past 100 years the wild blueberry crop has been harvested with a hand rake that has a   design similar to a cranberry scoop. Harvesting losses using hand raking varied from crew to crew, but the range had been estimated at 15% to 40% with an overall average of 20%. One of the biggest problems encountered by blueberry rakers is interference from weeds which can result in reduced raking speed with many berries missed or spilled. The underlying factors for the development of mechanical harvester were the huge harvesting expense, shortage and quality of labor and short harvesting season. Challenges in development of a mechanical harvester were: uneven field topography, low plant height and the presence of many weeds, bare soil and other debris. The research on the development of the mechanical harvester started in early 1950s but a viable harvester was not produced until the 1980s. Many mechanical harvesting systems were developed during this time span but were not commercially adopted due to many unsolved technical difficulties such as rough terrain, impractical ability to achieve harvesting efficiency and mechanical damage to the fruit.

The first wild blueberry harvester was modified in 1956 from a mechanical cranberry picker consisting of a series of six raking combs that raked in a direction opposite to the travel of the machine with high fruit loss and digging of soil during harvesting. The hollow reel raking mechanism was developed which served as the basis of harvesters today. The picking efficiency of this machine was 80 to 85% of the berries on the vine although it could only pick 30 to 35% of the fields due to the limitations in field terrain. The picking efficiency of a harvester was defined as a ratio of the weight of harvested berries to the weight of berries on the plants before harvesting. It was reported that the wild blueberry harvester picked better on the smooth ground with no weeds, but it experienced performance efficiency problems in rough and weedy fields. Doug Bragg Enterprises (DBE) Limited, in Collingwood, Nova Scotia achieved a great success by further adding hydraulic control systems for the head, head rotational speed, speed control of belts and conveyors, and the width of the picking head to improve the design.

It was estimated that the DBE blueberry harvester attains 68% (Weedy fields) to 75% (Smooth weed free fields) of total berry yields which is similar to manual raking. An engineering assessment test of the DBE blueberry harvester was performed it was found that this harvester was 69% efficient. The lower harvesting efficiency was due to the worn rollers and high ground speed of the chosen machine. Researchers suggested conducting a study on evaluation of harvester at various ground speeds and header rpm to analyze the sensitivity of machine operating parameters on picking effectiveness of the commercial wild blueberry harvester. Therefore, PA Team took the initiative and mounted a digital color camera on a wild blueberry harvester to estimate pre-harvest yield to quantify overall losses. Results of their study emphasized the need to quantify the berry losses during harvesting in variable wild blueberry fields.

In the last two decades, improved management practices using selective herbicides, fertilizers and pesticides have resulted in healthy and tall plants, higher plant density, tall weeds and significant increases in fruit yield. The wild blueberry industry is facing increased harvesting losses because of these changes in crop conditions. Therefore, the objective of PA team was to evaluate the existing commercial wild blueberry harvester for fruit losses during harvesting to determine the ideal combination of ground speed and header rpm for the most efficient fruit recovery and to identify the relationships among the berry losses and measured parameters.