# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
###### Abstract  
Uniform spatial distribution of plants is crucial in arable crops. Seeding quality is affected by numerous parameters, including the working speed and vibrations of the seeder. Therefore, investigating effective and rapid methods to evaluate seeding quality and the parameters affecting the seeders' performance is of high importance. With the latest advancements in unmanned aerial vehicle (UAV) technology, the potential for acquiring accurate agricultural data has significantly increased, making UAVs an ideal tool for scouting applications in agricultural systems. This study investigates the effectiveness of utilizing different plant recognition algorithms applied to UAV-derived images for evaluating seeder performance based on detected plant spacings. Additionally, it examines the impact of seeding unit vibrations on seeding quality by analyzing accelerometer data installed on the seeder. For the image analysis, three plant recognition approaches were tested: an unsupervised segmentation method based on the Visible Atmospherically Resistant Index (VAR), template matching (TM), and a deep learning model called Mask R-CNN. The Mask R-CNN model demonstrated the highest recognition reliability at 96.7%, excelling in detecting seeding errors such as misses and doubles, as well as in evaluating the quality of feed index and precision when compared to ground-truth data. Although the VARI-based unsupervised method and TM outperformed Mask R-CNN in recognizing double spacings, overall, the Mask R-CNN was the most promising. Vibration analysis indicated that the seeder's working speed significantly affected seeding quality. These findings suggest areas for potential improvements in machine technology to improve sowing operations. U  
20 October 202414 November 20241  
seeding errors increases the likelihood of weed proliferation [5]. With the introduction of heavy-weight agriculture machinery, field conditions have become more difficult (soil is less homogenous and more compacted with a greater presence of plant residue), and high accuracy of seeding and uniform plant germination are far more complex to achieve [6]. The assessment of seeding quality for wide-row crops is conducted based on the guidelines outlined in ISO 7256/1 [7]. Sowing quality may be determined in the field either immediately after seeding or after plant germination. Both methods are time- and labor-demanding and include certain disadvantages in terms of limited sample size, seed in-furrow site disturbance during digging, reduction of plant population by pests, etc. With the technological breakthrough of the last decade, UAV technology, in conjunction with image processing techniques, provides detailed insight into the field [8]. UAVs provide several benefits, including the ability to deploy swiftly, non-invasive observation with ultrahigh spatial resolution, and frequent data collection [9]. UAVs offer effective and repetitive measurements of relatively large fields [10]. Also, many efforts have been undertaken in the development of various algorithms for pattern recognition from aerial images with different expectations of weed mapping [11], canopy 3D mapping [12], plant density [13], plant row segmentation [14], etc. Plant counting from images captured in different growth stages has received certain attention in recent times since it is one of the most contributing factors to the grain yield of field crops [15]. Spatial plant density determination instead of pure average plant population has to be accounted for when yield variability is observed. Irregular plant density can indicate pest presence, local soil conditions (e.g., waterlogging or soil crusting), or seeder malfunction. Evaluating plant density is crucial, especially for winter crops in the northern hemisphere. These crops are prone to a decrease in population after the cold winter period. Therefore, in the spring, the nitrogen management strategy should be tailored to the actual plant density. This will help optimize tillage, plant growth, and nitrogen use efficiency [16; 17].