SilageSnap

Name: SilageSnap
Author: University of Wisconsin-Madison

University of Wisconsin-Madison

PROVEN

Validated with strong research evidence

For:Industry Professionals

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App Summary

SilageSnap is a mobile assessment tool for farmers, dairy nutritionists, and custom harvesters that uses an image-processing algorithm to provide a real-time, in-field evaluation of corn silage Kernel Processing Score (KPS). The associated research demonstrated that the app's image-based KPS estimations are well correlated with both the standard laboratory sieving method (r(23)=0.8) and an in-situ measure of feed quality (r(10)=0.77). The authors conclude that this tool provides novel, in-field information that allows users to make immediate machinery adjustments to improve overall silage quality.

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Detailed Description

Functionality & Mechanism

Developed by the University of Wisconsin-Madison, SilageSnap provides in-field estimation of corn silage Kernel Processing Score (KPS). The system leverages a proprietary image-processing algorithm to analyze kernel particle size. Following hydrodynamic separation of kernels from plant material, an image is captured with a coin for scale. The algorithm quantifies particle size distribution to calculate an estimated KPS, delivering an immediate qualitative assessment. The system stores quantitative results and the source image, facilitating export for further analysis.

Evidence & Research Context

A validation study demonstrated the underlying image-analysis algorithm's KPS estimations are strongly correlated with the standard mechanical sieving method (r(23) = 0.8, p < 0.001).
The algorithm-derived KPS for fresh samples was also highly correlated with in situ dry matter disappearance in ruminally cannulated dairy cows (r(10) = 0.77).
The method can statistically differentiate mean KPS results from samples processed with different crop processor gap settings (1-4 mm, P = 0.014), indicating sensitivity to machine adjustments.

Intended Use & Scope

This tool is designed for agricultural professionals, including farmers, dairy nutritionists, and custom harvesters, for rapid, in-field assessment of silage processing quality. Its primary utility is to provide actionable data for real-time adjustment of harvesting machinery. The app provides an estimation of KPS and does not replace definitive laboratory analysis for feed formulation.

Studies & Publications

2 publications

Peer-reviewed research associated with this app.

Validation Study

Predicting in situ dry matter disappearance of chopped and processed corn kernels using image-analysis techniques

Luck et al. (2020) · Applied Animal Science

App accurately estimated corn kernel processing quality, matching laboratory measurements of starch availability.

Objective Corn silage processing score (CSPS) is a well-known and often-used indicator of starch availability in whole-plant corn silage. However, obtaining results from a laboratory can take days or more. The objective of this work was to test an image-analysis method as a tool for quantitative assessment of corn kernel particle size and feed quality during harvest. Materials and Methods Kernel processor gap settings of 1, 2, 3, and 4 mm were assessed using the standard sieving method and with an image-analysis method. In situ slowly disappearing DM in ruminally cannulated lactating dairy cows was also assessed at the various kernel processor gap settings and compared with the 2 CSPS estimation methods: image analysis and sieving. Results and Discussion The image-analysis method was able to statistically separate mean estimated CSPS (P = 0.014) across the different crop processor gap settings for fresh samples. Image analysis CSPS estimation of fresh samples was highly correlated with in situ DM disappearance results [r(10) = 0.77] using a 12-h incubation time. These results indicate that image processing is a viable tool for estimating CSPS and feed quality. Implications and Applications A smartphone application, SilageSnap, was developed that uses the image-processing algorithm to estimate CSPS. This novel tool provides in-field estimation of CSPS that translates into actionable information regarding feed quality to inform machine adjustment decisions, which farmers, dairy nutritionists, and custom harvesters have not had in the past.

Validation Study

Predicting kernel processing score of harvested and processed corn silage via image processing techniques

Drewry et al. (2019) · Computers and Electronics in Agriculture

Image analysis reliably measured corn kernel particle size, matching standard laboratory sieving method.

An image processing algorithm was developed to characterize the size distribution of corn kernel particles from Whole Plant Corn Silage (WPCS). The algorithm determines particle cross sectional area and maximum inscribed circle diameter as well as cumulative undersize percent, dimensions of significance, and key characteristics of the distribution including mean particle size, skewness, and kurtosis. Kernel Possessing Score (KPS) was derived from the area-weighted cumulative undersize percent of 4.75?mm. Samples of WPCS harvested using self-propelled forage harvester with crop processing roll gap clearance of 1, 2, 3, and 4?mm were analyzed in their fresh, dry, and sieved states. Algorithm results were compared with the standard method of mechanical sieving and found to be well correlated r(23)?=?0.8, p?<?0.001. Additionally, analysis of particles before and after sieving indicated that sieving significantly increased KPS for 1 (t(5)?=?6.6, p?=?0.001), 2 (t(5)?=?4.2, p?=?0.01), and 3 (t(5)?=?3.5, p?=?0.02) mm samples. This algorithm has the potential to more accurately determine KPS in field compared to currently available methods, allowing for adjustment of kernel processing during harvest which will improve silage quality.

In the Media

UW Changes Lives: Let your phone do the farming

UW-Madison College of Agricultural and Life Sciences developed SilageSnap along with other farming apps to help farmers record and process data directly in the barn or on the tractor using smartphones. Associate Dean William Barker explains that "these types of apps are so valuable in powering decision-making at a time of great challenges to food production systems," particularly helping small farms avoid being "left behind or priced out of the revolution of digital agriculture." The apps are available for both Apple iOS and Android devices, with some offering Spanish language support.

WiscRead article

App helps farmers make the most of their corn harvest

University of Wisconsin-Madison researchers Brian Luck and Rebecca Willett developed SilageSnap to help farmers assess corn cracking quality in the field using just a smartphone and handful of harvested corn. "Cracked corn makes the feed easier to digest, so cows can produce more milk," says Luck, noting that excellently cracked corn can boost milk production by up to two pounds per cow per day according to UW-Madison studies. The app solves the problem that farmers previously had no way to tell how well their harvesting machinery cracked kernels while still in the field.

WiscRead article