ALBERT

Description: Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (〉8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

Description: Mechanical harvesting of wild blueberries remains the most cost-effective means for harvesting the crop. Harvesting of wild blueberries is heavily reliant on operator skill and full automation of the harvester will rely on precise and accurate determination of the picking reel’s height. This study looked at developing a control system which would provide feedback on harvester picking reel height on up to five harvester heads. Additionally, the control system looked at implementing three quality of life improvements for operators, operating multiple heads until the point when full automation is achieved. These three functions were a tandem movement function, a baseline function, and a set-to-one function. Each of these functions were evaluated for their precision and accuracy and returned absolute mean discrepancies of 3.10, 2.20, and 2.50 mm respectively. Both electric and hydraulic actuators were evaluated for their effectiveness in this system however, the electric actuator was simply too slow to be deemed viable for the commercial harvesters. To achieve the full 203.2 mm stroke required by the harvester head, the electric actuator required 13.96 s while the hydraulic actuator required only 2.30 s under the same load.

Description: The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study.

Description: Assessment of Global Navigation Satellite Signal (GNSS) autosteering is a critical step in the progression towards full wild blueberry (vaccinium angustifolium) harvester automation. The objective of the study was to analyze John Deere’s universal Auto-Trac 300 autosteer, 4640 display, and Starfire 6000 receiver with both the SF1 and SF3 signal levels for their pass-to-pass accuracy as well as how they compared versus a manual harvester operator. Incorporation of GNSS autosteer in wild blueberry harvesting has never been assessed as the slow harvester travel speeds and small working width caused the implementation to be too challenging. The results of this study concluded that there were no significant differences in pass-to-pass accuracy based on travel speeds of 0.31 m s−1, 0.45 m s−1, and 0.58 m s−1 (p = 0.174). Comparing the signal levels showed significantly greater accuracy of the SF3 system (p 〈 0.001), which yielded an absolute mean pass-to-pass accuracy 22.7 mm better than SF1. Neither the SF1 nor SF3 signal levels were able to reach the levels of accuracy advertised by the manufacturer. That said, both signal levels performed better than a manual operator (p 〈 0.001). This result serves to support the idea that in the absence of skilled operators, an autosteer system can provide significant support for new operators. Further, an autosteer system can allow any operator to focus more of their attention on operating the harvester head and properly filling storage bins. This will lead to higher quality berries with less debris and spoilage. The results of this study are encouraging and represent a significant step towards full harvester automation for the wild blueberry crop.

Description: Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two weeds, hair fescue and sheep sorrel, in images of wild blueberry fields. Commercial herbicide sprayers provide a uniform application of agrochemicals to manage patches of these weeds. Three object-detection and three image-classification CNNs were trained to identify hair fescue and sheep sorrel using images from 58 wild blueberry fields. The CNNs were trained using 1280x720 images and were tested at four different internal resolutions. The CNNs were retrained with progressively smaller training datasets ranging from 3780 to 472 images to determine the effect of dataset size on accuracy. YOLOv3-Tiny was the best object-detection CNN, detecting at least one target weed per image with F1-scores of 0.97 for hair fescue and 0.90 for sheep sorrel at 1280 × 736 resolution. Darknet Reference was the most accurate image-classification CNN, classifying images containing hair fescue and sheep sorrel with F1-scores of 0.96 and 0.95, respectively at 1280 × 736. MobileNetV2 achieved comparable results at the lowest resolution, 864 × 480, with F1-scores of 0.95 for both weeds. Training dataset size had minimal effect on accuracy for all CNNs except Darknet Reference. This technology can be used in a smart sprayer to control target specific spray applications, reducing herbicide use. Future work will involve testing the CNNs for use on a smart sprayer and the development of an application to provide growers with field-specific information. Using CNNs to improve agricultural efficiency will create major cost-savings for wild blueberry producers.