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Subject: image processing

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    Agricultural Field Applications of Digital Image Processing Using an Open Source ImageJ Platform
    (North Dakota State University, 2019) Shajahan, Sunoj
    Digital image processing is one of the potential technologies used in precision agriculture to gather information, such as seed emergence, plant health, and phenology from the digital images. Despite its potential, the rate of adoption is slow due to limited accessibility, unsuitability to specific issues, unaffordability, and high technical knowledge requirement from the clientele. Therefore, the development of open source image processing applications that are task-specific, easy-to-use, requiring fewer inputs, and rich with features will be beneficial to the users/farmers for adoption. The Fiji software, an open source free image processing ImageJ platform, was used in this application development study. A collection of four different agricultural field applications were selected to address the existing issues and develop image processing tools by applying novel approaches and simple mathematical principles. First, an automated application, using a digital image and “pixel-march” method, performed multiple radial measurements of sunflower floral components. At least 32 measurements for ray florets and eight for the disc were required statistically for accurate dimensions. Second, the color calibration of digital images addressed the light intensity variations of images using standard calibration chart and derived color calibration matrix from selected color patches. Calibration using just three-color patches: red, green, and blue was sufficient to obtain images of uniform intensity. Third, plant stand count and their spatial distribution from UAS images were determined with an accuracy of ≈96 %, through pixel-profile identification method and plant cluster segmentation. Fourth, the soybean phenological stages from the PhenoCam time-lapse imagery were analyzed and they matched with the manual visual observation. The green leaf index produced the minimum variations from its smoothed curve. The time of image capture and PhenoCam distances had significant effects on the vegetation indices analyzed. A simplified approach using kymograph was developed, which was quick and efficient for phenological observations. Based on the study, these tools can be equally applied to other scenarios, or new user-coded, user-friendly, image processing tools can be developed to address specific requirements. In conclusion, these successful results demonstrated the suitability and possibility of task-specific, open source, digital image processing tools development for agricultural field applications.
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    Soybean Leaf Chlorophyll Estimation and Iron Deficiency Field Rating Determination at Plot and Field Scales Through Image Processing and Machine Learning
    (North Dakota State University, 2020) Hassanijalilian, Oveis
    Iron deficiency chlorosis (IDC) is the most common reason for chlorosis in soybean (Glycine max (L.) Merrill) and causes an average yield loss of 120 million dollars per year across 1.8 million ha in the North Central US alone. As the most effective way to avoid IDC is the use of tolerant cultivars, they are visually rated for IDC by experts; however, this method is subjective and not feasible for a larger scale. An alternate more objective image processing method can be implemented in various platforms and fields. This approach relies on a color vegetation index (CVI) that can quantify chlorophyll, as chlorophyll content is a good IDC indicator. Therefore, this research is aimed at developing image processing methods at leaf, plot, and field scales with machine learning methods for chlorophyll and IDC measurement. This study also reviewed and synthesized the IDC measurement and management methods. Smartphone digital images with machine learning models successfully estimated the chlorophyll content of soybean leaves infield. Dark green color index (DGCI) was the best-correlated CVI with chlorophyll. The pixel count of four different ranges of DGCI (RPC) was used as input features for different models, and the support vector machine produced the highest performance. Handheld camera images of soybean plots extracted DGCI, which mimicked visual rating, and canopy size that were used as inputs to decision-tree based models for IDC classification. The AdaBoost model was the best model in classifying IDC severity. Unmanned aerial vehicle soybean IDC cultivar trial fields images extracted DGCI, canopy size, and their product (CDP) for IDC severity monitoring and yield prediction. The area under the curve (AUC) was employed to aggregate the data into a single value through time, and the correlation between all the features and yield was good. Although CDP at latest growth stage had the highest correlation with yield, AUC of CDP was the most consistent index for soybean yield prediction. This research demonstrated that digital image processing along with the machine learning methods can be successfully applied to the soybean IDC measurement and the various soybean related stakeholders can benefit from this research.