Corn Date of Planting and Depth

The exceptionally wet weather in 2019 impacted corn yield. Excessive rainfall reduced corn emergence and plant stand. Many production fields were replanted due to poor stand from flooding. In this study, corn that was planted too shallow (1 inch) or too deep (3 inches) had less yield than that planted at 2-inch depth. The best yield was observed in the corn planted on April 16, 2019. The results from this record wet year were different from previous years, when early planted corn had higher yields

2014 Crop Performance in Southeast Kansas

Crop variety testing determines the production potential of newly released crop cultivars in Southeast Kansas. The genetic potential is moderated by environmental conditions during the growing season as well as soil productive capacity

Neuromuscular Control Modelling of Human Perturbed Posture Through Piecewise Affine Autoregressive With Exogenous Input Models

In this study, the neuromuscular control modeling of the perturbed human upright stance is assessed through piecewise affine autoregressive with exogenous input (PWARX) models. Ten healthy subjects underwent an experimental protocol where visual deprivation and cognitive load are applied to evaluate whether PWARX can be used for modeling the role of the central nervous system (CNS) in balance maintenance in different conditions. Balance maintenance is modeled as a single-link inverted pendulum; and kinematic, dynamic, and electromyography (EMG) data are used to fit the PWARX models of the CNS activity. Models are trained on 70% and tested on the 30% of unseen data belonging to the remaining dataset. The models are able to capture which factors the CNS is subjected to, showing a fitting accuracy higher than 90% for each experimental condition. The models present a switch between two different control dynamics, coherent with the physiological response to a sudden balance perturbation and mirrored by the data-driven lag selection for data time series. The outcomes of this study indicate that hybrid postural control policies, yet investigated for unperturbed stance, could be an appropriate motor control paradigm when balance maintenance undergoes external disruption

Corn Planting Date and Depth – Impacts on Yield

Corn growth and production is dependent on environmental conditions during the growing season. Optimal corn growth occurs between 50 and 86°F. Early-season soil temperatures may reduce corn emergence. Conversely, later-planted corn may not have adequate moisture for good pollination and grain production. This research tested the impact of planting date and planting depth on corn yield. The yield decreased with later planting dates. Earlier planting dates had better yield at lower planting depths, but yield was reduced at deeper planting depths at later planting

A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb

In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, the angular kinematics of hip and ankle joints were synchronously recorded with sEMG signal for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals, i.e. time domain (TD) and wavelet (WT) and compared to have a compromise between the reliability and computational capacity, they were used for feeding three regression models, i.e. Artificial Neural Networks, Random Forest, and Least Squares - Support Vector Machine (LS-SVM). BF together with LS-SVM provided the best ankle angle estimation in both TD and WT domains (RMSE < 5.6 deg). The inclusion of Hip joint trajectory significantly enhanced the regression performances of the model (RMSE < 4.5 deg). Results showed the feasibility of estimating the ankle trajectory using only proximal and limited information from the lower limb which would maximize a potential transfemoral amputee user's comfortability while facing the challenge of having a small amount of information thus requiring robust data-driven models. These findings represent a significant step towards the development of a minimal setup useful for the control design of ankle active prosthetics and rehabilitative solutions

A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy from Standing Balance by Leveraging Multi-Domain Features

The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease

Crop Production 2020 – Corn, Sorghum, Soybean, and Sunflower Variety Testing

This is a summary of the variety testing for corn, sorghum, soybean, and sunflower. Nine corn varieties were tested in 2020, with an average yield of 107.6 bu/a. Twenty-four cultivars of soybeans from maturity groups (MG) 3-4 and twenty-seven cultivars from MG4-5 were tested in both full-season and double-cropped management. Full-season beans yielded an average of 54.5 bu/a for MG3-4 and 58.8 bu/a for MG4-5, which was greater than the average yields in the double-cropped beans at 32 bu/a for MG3-4 and 40.5 bu/a for MG4-5. The state-wide average soybean yield in 2020 was higher than the 10-year average. Nine cultivars of oilseed sunflowers yielded 1307 lb/a across all cultivars, slightly below the 10-year state average yield

Southeast Kansas Crop Production Summary – 2018

This is a summary of the crop production conditions in southeast Kansas in 2018, and the results of the variety testing for corn, soybean, sorghum, sunflower, and wheat

Southeast Kansas Wheat Variety Test Results - 2020

This is a summary of the winter wheat production conditions in southeast Kansas in 2019-2020 and the results of the variety testing. Fifteen hard red and ten soft red winter wheat varieties were compared for yield and test weight. High spring rainfall increased disease pressure; cultivars were rated for Fusarium head blight and stripe rust. Average yield of hard red wheat varieties was above average at 81.1 bu/acre across all varieties. Soft red wheat yield was 102.4 bu/acre across all varieties. For comparison, previous variety yield results are reported from 2016, 2017, and 2018

Intelligent Human–Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios