Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing

Potato is a major food crop in China. Its development and nutritional state can be inferred by the content of chlorophyll in its canopy. However, the existing study on applying feature extraction and optimization algorithms to determine the canopy SPAD (Soil-Plant Analytical Development) values of potatoes at various fertility stages is inadequate and not very reliable. Using the Pearson feature selection algorithm and the Competitive Adaptive Reweighted Sampling (CARS) method, the Vegetation Index (VI) with the highest correlation was selected as a training feature depended on multispectral orthophoto images from unmanned aerial vehicle (UAV) and measured SPAD values. At various potato fertility stages, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) inversion models were constructed. The models' parameters were then optimized using the Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA). The findings demonstrated a higher correlation between the feature selected VI and SPAD values; additionally, the optimization algorithm enhanced the models' prediction accuracy; finally, the addition of the fertility stage feature considerably increased the accuracy of the full fertility stage in comparison to the single fertility stage. The models with the highest inversion accuracy were the CARS-SSA-RF, CARS-SSA-XGBoost, and Pearson-SSA-XGBoost models. For the single-fertility and full-fertility phases, respectively, the optimal coefficients of determination (R2s) were 0.60, 0.66, and 0.87, the root-mean-square errors (RMSEs) were 2.63, 3.23, and 2.39, and the mean absolute errors (MAEs) were 2.00, 2.75, and 1.99.