Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study

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 (R 2) 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 (R 2 ¼ 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.

High-resolution unmanned aircraft systems imagery for stay-green characterization in grain sorghum (Sorghum bicolor L.)

“…To increase crop yields to meet a growing population, breeders must rely on methods to screen crops quickly and thoroughly for desired traits of interest. 80 In this paper, we have introduced a methodology for sorghum researchers to follow to collect, process, extract, and analyze multispectral data for more efficient screening of large-scale grain sorghum breeding plots for traits associated with SG. As UAS have become a more cost-effective method of obtaining very high spatial resolution imagery for such evaluations, 81 there is a need for specific methodologies to be developed for sorghum breeders to use this technology for rapid identification of the SG trait.…”

Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN

“…However, an assumption of a linear or polynomial relationship between the crop yield and environmental factors is not always valid. Partial least squares regression (PLSR) has been applied in [ 15 ], for modelling the yield of snap bean based on the data collected from hyperspectral sensing. Neural networks have also been widely applied for greenhouse crop yield prediction.…”

Predicting Table Beet Root Yield with Multispectral UAS Imagery

“…The area-augmented procedure used here could then be applied to model scalable yield components. A future study may also use hyperspectral data derived from an UAS or handheld spectrometer, with the goal of determining a set of spectral bands that exhibit optimal association with yield components [55], thereby improving upon the multispectral bands used in this study.…”