Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R2 < 0.1) than similar measures from the multispectral sensors (R2 < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions

Lifestyle, exercise and activity package for people living with progressive multiple sclerosis (LEAP-MS) : protocol for a single-arm feasibility study

Background. We have co-designed a tailored blended physiotherapy intervention for people with Progressive Multiple Sclerosis (PwPMS) who often struggle to access support for physical activity. Underpinned by self-management principles, the Lifestyle, Exercise and Activity Package for people with Multiple Sclerosis (LEAP-MS) intervention, incorporates face-to-face or online physiotherapy coaching sessions with an accompanying online physical activity platform. The LEAP-MS platform is a multi-user system enabling user and physiotherapist to co-create activity plans. The LEAP-MS platform consists of an information and activity suite, interactive components enabling selection of exercises into an activity programme, goal setting, and activity logging. The platform also facilitates online remote support from a physiotherapist through an embedded online messaging function. We aim to evaluate the LEAP-MS platform in a feasibility trial

Developing the Stroke Exercise Preference Inventory (SEPI)

<div><p>Background</p><p>Physical inactivity is highly prevalent after stroke, increasing the risk of poor health outcomes including recurrent stroke. Tailoring of exercise programs to individual preferences can improve adherence, but no tools exist for this purpose in stroke.</p><p>Methods</p><p>We identified potential questionnaire items for establishing exercise preferences via: (i) our preliminary Exercise Preference Questionnaire in stroke, (ii) similar tools used in other conditions, and (iii) expert panel consultations. The resulting 35-item questionnaire (SEPI-35) was administered to stroke survivors, along with measures of disability, depression, anxiety, fatigue and self-reported physical activity. Exploratory factor analysis was used to identify a factor structure in exercise preferences, providing a framework for item reduction. Associations between exercise preferences and personal characteristics were analysed using multivariable regression.</p><p>Results</p><p>A group of 134 community-dwelling stroke survivors (mean age 64.0, SD 13.3) participated. Analysis of the SEPI-35 identified 7 exercise preference factors (<i>Supervision-support</i>, <i>Confidence-challenge</i>, <i>Health-wellbeing</i>, <i>Exercise context</i>, <i>Home-alone</i>, <i>Similar others</i>, <i>Music-TV</i>). Item reduction processes yielded a 13-item version (SEPI-13); in analysis of this version, the original factor structure was maintained. Lower scores on <i>Confidence-challenge</i> were significantly associated with disability (p = 0.002), depression (p = 0.001) and fatigue (p = 0.001). Self-reported barriers to exercise were particularly prevalent in those experiencing fatigue and anxiety.</p><p>Conclusions</p><p>The SEPI-13 is a brief instrument that allows assessment of exercise preferences and barriers in the stroke population. This new tool can be employed by health professionals to inform the development of individually tailored exercise interventions.</p></div

Means, standard deviations and percentages of those reaching varying thresholds of agreement for the 9 exercise barrier items.

<p>Means, standard deviations and percentages of those reaching varying thresholds of agreement for the 9 exercise barrier items.</p

Demographic and stroke characteristics of the participants.

<p>Demographic and stroke characteristics of the participants.</p

Item loadings from exploratory factor analysis of the SEPI-13 (varimax rotation with Kaiser normalisation, fixed at 7 factors), n = 126, coefficients <0.50 are suppressed.

<p>Unweighted factor scores (% agreement) are shown as mean and standard deviation.</p