With recent developments allowing increased planter speeds, improving spatial uniformity of crop emergence will continue to be an important focus of research. Vertical vibration during planting usually interferes with the seed metering and delivery process, and thereby affects seeding quality. Two different row crop planters were instrumented with accelerometers to monitor vertical vibration of planter row units in five fields with a total area of 220 ha in both no-till and strip-tilled conditions. The test results showed that the row unit vibration linearly increased with planting speeds. The R² of each fitting equation was more than 0.90. The main frequencies of the vibration were concentrated in a low-frequency band of 3 Hz to 10 Hz. These frequencies did not show an increasing trend with the planter speed. However, the amplitude clearly increased when the planter speed increased. For the John Deere MaxEmerge™ 5 planter, the manually measured average plant spacing was close to the target plant spacing with a maximum error of 21 mm, and a max coefficient of variation (CV) of 24.3%; the standard deviation (SD) increased with travel speed and row unit vibration. For the John Deere ExactEmerge™ planter, the plant spacing was closer to the target plant spacing with a maximum error of only 5 mm and a max CV of 14.3%; the SD remained almost constant, at lower values than that of the MaxEmerge™ 5, at different speeds. For both planters, overall, the row unit vibration increased planting spatial variability. The quality of feed index decreased with increased vertical acceleration on the row unit while the miss index increased. However, the multiples index was generally unaffected by row unit vibration.

Orchard target-oriented variable rate spraying is an effective method to reduce pesticide drift and excessive residues. To accomplish this task, the orchard targets’ characteristic information is needed to control liquid flow rate and airflow rate. One of the most important characteristics is the canopy density. In order to establish the canopy density model for a planar orchard target which is indispensable for canopy density calculation, a target density detection testing system was developed based on an ultrasonic sensor. A time-domain energy analysis method was employed to analyze the ultrasonic signal. Orthogonal regression central composite experiments were designed and conducted using man-made canopies of known density with three or four layers of leaves. Two model equations were obtained, of which the model for the canopies with four layers was found to be the most reliable. A verification test was conducted with different layers at the same density values and detecting distances. The test results showed that the relative errors of model density values and actual values of five, four, three and two layers of leaves were acceptable, while the maximum relative errors were 17.68%, 25.64%, 21.33% and 29.92%, respectively. It also suggested the model equation with four layers had a good applicability with different layers which increased with adjacent layers.

In the U.S., peanut farmers receive premium prices for crops with high seed grades. One component of seed grade is the proportion of seed weight to that of pod hulls and other matter. Seed weight and size are also important traits for food processors. Current methods for evaluating peanut seed grade require the opening of the pod and are time-consuming and labor-intensive. In this study, a non-destructive and efficient method to determine peanut seed weights was investigated. X-ray images of a total of 513 peanut pods from three commercial cultivars, each representing three market types, were taken using a soft X-ray imaging system. The region of interest of each image, the seeds, was extracted two ways, manually and with a differential evolution segmentation algorithm. The comprehensive attenuation index (CAI) value was calculated from the segmented regions of interest. Lastly, linear regression models were established between peanut seed weights and the CAI. The results demonstrated that the X-ray imaging technology, coupled with the differential evolution segmentation algorithm, may be used to estimate seed weights efficiently from intact peanut pods.