Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Abstract

Leaf area index (LAI) is a fundamental indicator of crop growth status, timely and non-destructive estimation of LAI is of significant importance for precision agriculture. In this study, a multi-rotor UAV platform equipped with CMOS image sensors was used to capture maize canopy information, simultaneously, a total of 264 ground‐measured LAI data were collected during a 2-year field experiment. Linear regression (LR), backpropagation neural network (BPNN), and random forest (RF) algorithms were used to establish LAI estimation models, and their performances were evaluated through 500 repetitions of random sub-sampling, training, and testing. The results showed that RGB-based VIs derived from UAV digital images were strongly related to LAI, and the grain-filling stage (GS) of maize was identified as the optimal period for LAI estimation. The RF model performed best at both whole period and individual growth stages, with the highest R2 (0.71–0.88) and the lowest RMSE (0.12–0.25) on test datasets, followed by the BPNN model and LR models. In addition, a smaller 5–95% interval range of R2 and RMSE was observed in the RF model, which indicated that the RF model has good generalization ability and is able to produce reliable estimation results.

Physiological Responses of Indigenous Vegetable of Sintrong (Crassocephalum crepidioides) due to Exposure to High Temperature

“…The closer the spacing, the higher the leaf density and the lower the exposure to sunlight reaching the lower leaf layers so LAI value increases (Du et al, 2022). The inhibition of leaf expansion will have an impact on decreasing the capacity of the leaves to absorb light, so that it will minimize the performance of the rate of photosynthesis in plants.…”

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

“…Additionally, using handheld sensors (SPAD(Japan), Greenseeker(America), Li-cor(America) et al) can indirectly evaluate crop N status at the small scale level, which cannot comprehensively represent crop canopy in the field or at the large scale level [4][5][6]. Remote sensing technology based on an unmanned aerial vehicle (UAV) has been widely used for image collection to evaluate crop growth conditions [7][8][9][10][11][12]. Most studies used RGB, near-infrared, multispectral, hyperspectral, and thermal infrared cameras or sensors based on UAVs for crop monitoring at a large scale [8][9][10][11][12][13][14].…”

“…Remote sensing technology based on an unmanned aerial vehicle (UAV) has been widely used for image collection to evaluate crop growth conditions [7][8][9][10][11][12]. Most studies used RGB, near-infrared, multispectral, hyperspectral, and thermal infrared cameras or sensors based on UAVs for crop monitoring at a large scale [8][9][10][11][12][13][14]. Due to its affordability, flexibility, and high efficiency, as well as its band alignment, the RGB camera is mostly used in crop monitoring compared to multispectral and hyperspectral sensors, which indicates that the RGB camera based on UAVs has great potential for crop monitoring [15,16].…”

YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method

“…In recent years, advances in unmanned aerial vehicle (UAV) technology have provided a means to quickly and efficiently acquire high-throughput plant image data, enabling intelligent monitoring of massive plant growth processes [8]. Researchers use UAV-carrying sensors to collect data on plant height [9][10], leaf number [11][12], leaf area [13][14], etc., enabling real-time monitoring of plant growth and development as well as predicting yield. [15] showed an accuracy rate of 94.4% when using a camera-carrying UAV to count the number of pineapple crowns.…”