Reducing farmer uncertainty in spring forage harvests: image recognition to predict alfalfa-grass stand composition

Project Overview

Project Type: Graduate Student
Funds awarded in 2011: $14,997.00
Projected End Date: 12/31/2013
Grant Recipient: Cornell University
Region: Northeast
State: New York
Graduate Student:
Faculty Advisor:
Dr. Debbie Cherney
Cornell University
Faculty Advisor:
Jerome H. Cherney
Dept of Soil, Crop, and Atmospheric Science, Cornell University

Annual Reports


  • Agronomic: general hay and forage crops, grass (misc. perennial), hay
  • Animal Products: dairy


  • Animal Production: feed/forage, feed formulation, feed rations, stockpiled forages
  • Crop Production: food product quality/safety
  • Education and Training: decision support system, demonstration, extension, on-farm/ranch research, workshop
  • Farm Business Management: budgets/cost and returns, whole farm planning
  • Production Systems: integrated crop and livestock systems
  • Sustainable Communities: sustainability measures

    Proposal abstract:

    Perennial forages use large quantities of nutrients, minimizing the risk of nutrient leaching or runoff. High quality forage is also very competitive economically compared to grains. Although it is possible to make hay during spring in the Northeast, the odds are against it. Spring forage harvest for silage is the most crucial time of the year, and sets the stage for good harvest management throughout the year. There is a relatively small range in optimal fiber content (NDF) for lactating dairy cows, making quality-related harvest management decisions critical. The purpose of this project is to improve the timing and nutritive value of spring forage harvests for dairy operations. Accurate prediction equations exist for estimating NDF content of mixed alfalfa-grass stands in spring, and estimating the optimal harvest date, but the weak link is estimating the proportion of grass (or alfalfa) in a stand. This project will acquire numerous digital images of alfalfa-grass stands and relate the images to the actual percentage of alfalfa and grass in the image area. A program will then be designed to allow farmers and consultants to accurately estimate alfalfa-grass proportion, stand NDF, and optimum harvest date. A digital picture of the stand and a measure of maximum alfalfa height will be the only required inputs to an internet program, accessible by computer or smart phone. Such a tool will allow farmers and consultants to prioritize the order of harvest of alfalfa-grass fields to maximize chances of obtaining optimal forage NDF for lactating dairy cow diets.

    Project objectives from proposal:

    Ultimate Objective:
    Improve the timing and quality of spring forage harvests for Northeast dairy farms by reducing uncertainty in the estimation of alfalfa-grass stand composition.

    Proximate Objectives:
    1) Capture digital images from representative samples of mixed stands of alfalfa-grass in Northeast farmers’ fields.

    2) Determine known stand composition values for each sample.

    3) Create a software system that can filter and normalize a mixed stand image, evaluate the image, and return the percentage grass and alfalfa in the stand.

    4) Generate a free web service on that will allow farmers to upload images acquired from their fields and receive stand composition results and predictions of optimal forage quality and harvest timing in a rapid manner.

    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.