ABSTRACT The accurate acquisition of crop planting area information allows for agricultural management departments to understand crop production information promptly. Concurrently, support vector machine (SVM) algorithms are unable to determine parameter combinations in remote sensing image crop classification to obtain optimal classification results. To solve this issue, this paper proposes an SVM that is optimized by the adaptive mutation particle swarm optimization algorithm. To test the algorithm, we undertook an experiment in Acheng District, Harbin City, Heilongjiang province, China, using Gaofen 1/wide-field-view satellite images to construct a time series for various vegetation indices. The SVM model with optimized parameters was compared with a traditional backpropagation neural network, a decision tree, and an SVM without parameter optimization. The experimental results verified that the improved SVM model obtained the highest classification accuracy.

ABSTRACT This study used a spectral index method and an artificial intelligence algorithm to quantitatively analyze rice canopy soil and plant analyzer development (SPAD) based on ground nonimaging spectral data and UAV hyperspectral images to build a high-precision SPAD prediction model for nondestructive monitoring of the chlorophyll relative content of rice in cold regions. First, this study First, this study selected characteristic bands sensitive to SPAD using uninformative variable elimination and the successive projections algorithm. Then, the correlation between commonly used vegetation indices and SPAD was analyzed. Finally, this study constructed a back propagation neural network (BPNN) model, BPNN with particle swarm optimization (PSO-BPNN) model, and BPNN with genetic algorithm optimization (GA-BPNN) model, and then verified the reliability of these models. According to the results, GA-BPNN had the best predictive effect. The coefficient of the determination reached 0.818, and the root mean square error was 0.847. GA-BPNN model combined with UAV hyperspectral images were used for inversion mapping; the predicted range of SPAD was 33.1–41.2, which is in good agreement with the measured value (32.7–40.6). The inversion of regional rice canopy SPAD by nonimaging spectral data and UAV hyperspectral images had high credibility, which provided technical support for the scientific management of rice in a cold region.