ABSTRACT. A machine vision-basedorn plant populations that are higher or lower than optimal can reduce crop yield. Duncan (1958) found that corn yield was maximized at particular plant populations depending on nutrient availability. Wiley and Heath (1969) investigated the relationships established by different researchers between corn population density and yield and found that the predictions had similar trends of yield maximization at particular plant population densities. Duncan (1984) presented the theory of crowding as a reason for yield reduction. However, optimum plant densities have not been constant over time but have increased substantially over the last several decades (Troyer and Rosenbrook, 1983;Nafziger, 1994).Even if a corn variety is planted at its optimal population, row spacing and interplant distance within a row can also affect the final yield. Plant population density, as well as interplant distribution, is important in effective utilization of available resources like nutrients and sunlight. Barbieri et al. (2000) studied the row spacing effect at different levels of nitrogen availability in corn. They found that the corn yield was higher when the row spacing was decreased for the same population density. The relative yield increase was higher for nitrogen-deficient fields. Doerge et al. (2002) measured spacing of 6,000 plants in research conducted in Missouri, Iowa, and Minnesota. The whole-field plant spacing standard deviation ranged from 3.2 to 6.9 inches. They estimated that every inch reduction in plant spacing standard deviation in a commercial field would increase the yield by about 3.4 bu/acre. Nafziger (1996) found that when there is a missing plant, the plants on either side compensated for only 47% of the reduced yield in lower population density fields (18,000 plants /acre) and 19% in higher plant density (30,000 plants/acre) fields, hence decreasing the final yield.There are three main causes of variability in plant spacing: seed germination, planter seed placement, and plant death. Seed germination rates typically range from 90% to 95% (Nielsen, 2001). Planter performance depends both on planter maintenance and speed. Nielsen (1995) reported that when the planter speed varied from 6.4 to 11.2 km/h (4 to 7 mph), the planted seed rate at higher speeds was significantly different than the planted seed rate at lower speeds. He concluded that a yield loss of at least 1.9 bu/acre occurs at every 1 mph speed increase in the range of 4 to 7 mph. Weather-and pest-related damage may result in unevenly spaced plant survivors within a row (Nielsen, 2001). Because of these factors, established plant population and spacing may be different than target plant population. Bullock et al. (1998) found that for variable-rate seeding to be profitable, a farmer needs extensive knowledge of site-specific plant population versus yield data from many years. Manual stand counts would not be feasible for a large field and are also susceptible to human error. An automated plant counting system provide...

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A texture-based weed classification method was developed. The method consisted of a low-level Gabor wavelets-based feature extraction algorithm and a high-level neural network-based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur, velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied. After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical implementation under real-time constraints.

Teaching sustainability concepts to multidisciplinary classes of engineering students is challenging due to their diverse background and discipline-specific skill set and the interdisciplinary nature of the sustainability issues at stake. The present study was conducted to understand the challenges and identify opportunities for improving teaching and learning of sustainability in higher education institutions. The case study used for data collection was the Sustainable Engineering and International Development course taught at Iowa State University since 2005. We assessed the students' course content knowledge before and after the course and their perceptions about the overall course, content, and instruction methods. A mixed methods approach consisting of qualitative (focus groups) and quantitative (survey and pre-and postassessment) techniques was used for the study. Quantitative data were analyzed using descriptive statistics, explanatory analysis, and multinomial logistic regression. Thematic analysis was used to evaluate the qualitative data. The difficulty level of the pre-and post-assessment was low. Application-based questions could be included in the assessment to test beyond the levels of mere comprehension and memorization. Student perceptions about module usefulness exhibited year-to-year variation, which was attributed to the diversity of students enrolled in the course each year. Barriers to learning in a multidisciplinary engineering course included student difficulties working in multidisciplinary teams, course organization, and limited student engagement in

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