Machine systems to enhance agronomic response in seeding technologies - Gigena Berretta, Bautista (Kansas State University, 2024)
The importance of proper planting and crop establishment cannot be underestimated, as they directly affect crop yield, quality, and overall agricultural productivity. Growers have varying field conditions and adopt different planting strategies, and each requires a unique approach to achieve desired agronomic responses. Hence, proper selection and adoption from existing precision planting technologies suited for their conditions is essential to optimize crop input resources and enhance crop yields. This research examined two essential aspects of precision planting systems and its application into two different growers’ situation. The first study involved assessing the effectiveness of row cleaners within air-seeders in high-residue environments, and, second study analyzes the performance of high-speed planting on no-till fields. A novel methodology based on a computer vision system was developed to objectively evaluate the effectiveness of row cleaners installed on single-disc air seeders in creating optimal seed furrow conditions for improving seed placement. Through the implementation of a tailor-made data acquisition system and the operation of an air seeder across different wheat residue conditions, the study demonstrated the feasibility of quantifying row cleaner operation in real time, exposing the possibility of assessing the performance of varying row cleaner models for air seeders on an unbiased and repeatable way, and also paving the way for automating control decisions during seeding processes. High-speed planting experiment involved comparing the performance of two precision planting systems on corn, evaluating various parameters, including planting accuracy, frame flexibility, overall productivity, downforce control, and section control. Findings revealed subtle differences between the two planting systems on productivity and planting quality. Still, there are notable differences on flexibility depending on the frame of each planter utilized as well as a slight difference in plant spacing. Overall, these studies contribute to advancing agricultural technology by providing objective methodologies for assessing and improving seeding and planting practices, ultimately enhancing efficiency and productivity in modern farming operations. The learning also set a baseline for future research on air seeder row cleaners and for upcoming research on row crop planter performance.
Keywords: Tractor; Precision agriculture; Planter; Computer vision; Productivity