Abstract
Farmers and growers typically use approaches based on the crop environment and local meteorology, many of which are labor-intensive, to predict crop yield. These approaches have found broad acceptance but lack real-time and physiological feedback for near-daily management purposes. This is true for broad-acre crops, such as snap bean, which is valued at hundreds of millions of dollars in the annual agricultural market. We aim to investigate the relationships between snap bean yield and plant spectral and biophysical information, collected using a hyperspectral spectroradiometer (400 to 2500 nm). The experiment focused on 48 single snap bean plants (cv. Huntington) in a controlled greenhouse environment during the growth period (69 days). We used applicable accuracy and precision metrics from partial least squares regression and cross-validation methods to evaluate the predictive ability of two harvest stages, namely an early-harvest and late-harvest stage, against our yield indicator (bean pod weight). Four different spectral data sets were used to investigate whether such oversampled, hyperspectral data sets could accurately and precisely model observed variability in yield, in terms of the coefficient of determination (R2) and root-mean-square error (RMSE). The objective of our approach hinges on the philosophy that selected spectral bands from this study, i.e., those that best explain yield variability, can be downsampled from a hyperspectral system for use in a more cost-effective, operational multispectral sensor. Our results suggested the optimal period for spectral evaluation of snap bean yield is 20 to 25 or 32 days prior to harvest for the early- and late-harvest stages, respectively, with the best model performing at a low RMSE (3.02 g plant − 1) and a high coefficient of determination (R2 = 0.72). An unmanned aerial systems-mounted, affordable, and wavelength-programmable multispectral imager, with bands corresponding to those identified, could provide a near real-time and reliable yield estimate prior to harvest.
Highlights
It is forecasted that the global population growth will approach ∼24% by the year 2050.1,2 This notion can profoundly alter social structures, from a society’s economic growth, to public health and our interaction with the environment; changes that could diminish employment opportunities, cause agricultural decline, and result in a continuous need for more productive and efficient farms.[3,4] As a result, our ability to use the available croplands, while optimizing yield, will become a requirement for sustainable use of scarce agricultural resources
Hassanzadeh et al.: Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study share in being the fifth largest vegetable crop nationally in terms of acreage, with 158,920 acres harvested for processing and 71,170 acres harvested for fresh market across the US in 2014, with a combined value of $416 million.[5]
For each data set, we evaluated the partial least squares regression (PLSR)-based coefficient of determination (R2), adjusted coefficient of determination (R2adj), and root-mean-square error (RMSE) of calibration and leave-one-out cross-validated sets,[72] based on the number of components used
Summary
It is forecasted that the global population growth will approach ∼24% by the year 2050.1,2 This notion can profoundly alter social structures, from a society’s economic growth, to public health and our interaction with the environment; changes that could diminish employment opportunities, cause agricultural decline, and result in a continuous need for more productive and efficient farms.[3,4] As a result, our ability to use the available croplands, while optimizing yield, will become a requirement for sustainable use of scarce agricultural resources. Hassanzadeh et al.: Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study share in being the fifth largest vegetable crop nationally in terms of acreage, with 158,920 acres harvested for processing and 71,170 acres harvested for fresh market across the US in 2014, with a combined value of $416 million.[5] Precision agriculture, or site-specific management, is one strategy to help satisfy this need by helping farmers to improve per-acre yield, reduce uncertainty and risk, and optimize the input–output crop yield continuum.[6,7] It is in the context of precision agriculture that remote sensing, a technology that collects spectral and structural information of an object remotely, has garnered attention over the past two decades.[8] One classification of remote sensing systems revolves around the platform, e.g., ground-based, airborne, or spaceborne, with the shared goal of nondestructively obtaining information.[9] This information can be acquired from sensing systems that generate color (RGB) images, three-dimensional (3-D)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
