Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

Abstract

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

“…In China, corn is one of the most significant food crops. Its planting area makes up 33.6% of the entire area used for food crops, while its production makes up 36.1% of the total grain yield [1]. Field weeds are one of the common disasters in corn production.…”

Temporal Changes of Leaf Spectral Properties and Rapid Chlorophyll—A Fluorescence under Natural Cold Stress in Rice Seedlings

“…Remote sensing is one of the possible ways to produce deep analyses about the current state of a plant in a non-destructive way. A lot of spectral data are typically produced by satellite [1][2][3], UAV [4][5][6] or handheld spectrometers [7]. The measurement of reflectance using portable spectral measurement instruments is the most advantageous method of data acquisition [8].…”

“…The nitrogen content of rice is correlated with the difference spectral index (DSI) [18]. There are also stressed and non-stressed difference indices [10,16,19] and temporal difference indices, such as between the stage of pre-flowering and ripening [5]. In addition to these, there are numerous vegetation indices that relate to the stress response.…”

Prediction of Fruit Traits of Camellia oleifera after Grafting Based on Deep Neural Networks

“…For example, it was estimated sugarcane yield by RF algorithm according to different prediction ranges and achieved good results [23]. It was also used to establish a rice yield estimation model and conduct model precision evaluation [24].…”