Non-destructive Method for Estimating Biomass of Plants Using Digital Camera Images

Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e.g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. For indirect biomass estimation, remote sensing data are used to determine agriculture species biomass using multiple regression analysis or Radiation Use Efficiency (RUE) models. In agriculture, RUE or Light Use Efficiency (LUE) is defined as dry biomass produced per unit of solar absorbed radiation or Photosynthetic Active Radiation. The LUE model needs a time series of NDVI index. Here, the lack of a few satellite images may make this time series incomplete. To overcome this deficiency, the farmer provided digital images that can be replaced for the missing satellite pixels/images that were deployed. Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial-based sensors. This study demonstrated a reliable, fast, and cost-effective approach for estimating NDVI using digital camera images. High-resolution digital images were acquired in the wheat field, and automated image processing methods were developed to segment the wheat canopy from the soil background. Exponential models for aboveground total NDVI showed acceptable precision and accuracy. Canopy cover estimated with images from digital cameras was sufficiently well correlated with satellite NDVI. Here, using a regression model, the NDVI index was estimated from the digital photographs. This method is named Digital NDVI (DNDVI). To develop this method, the relationship between the vegetation fractions (VF) obtained from the digital photos and the NDVI calculated from the satellite image of the same location were examined. For calculation of DNDVI to be used in cloudy days, the farmer is asked to supply a few photos from different parts of the farm (the number of photos depends on the size of the farm). These photos will be sent to the server where the VF values and then the averaged DNDVI will be calculated. The uncertainty of the DNDVI model in estimating biomass was 0.071 with relative RMSE of about 0.14. Next, wheat biomass was calculated using DNDVI and LUE model. The results of LUE model (and  in estimating biomass show a coefficient of determination (R2) 0.62 with an RMSE of 238 (gm-2). In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring wheat NDVI and growth status.