Agricultural sensors can greatly contribute to future technologies and systemic innovation in smart agriculture. However, the types and precision of sensors are limited to monitoring the agricultural environment with complex and diverse objects. The large and redundant monitoring data has also resulted in the low reliability of information perception. In this study, an improved radial basis function neural network (RBFNN) and Chernobyl disaster optimizer (ICDO) multi-sensor data fusion was proposed to improve the accuracy and reliability of single-sensor measurement. Firstly, an improved Chernobyl catastrophe optimization was performed on the neural network model. The good-point set theory was introduced to improve the initial population quality of the CDO, particularly for accuracy and speed. The adaptive Laplacian crossover operator was added to enhance the search performance. The better adaptive behavior was achieved in the high convergence speed. And then, the individual learning and differential evolution strategy were used to redefine the location update equation, in order to balance the local and global exploration. Secondly, the RBF neural network model was optimized by ICDO, in order to improve the stability of the model. Finally, the nonlinear mapping of the RBF neural network model was used to realize the multi-sensor data fusion with high accuracy. Three experiments were conducted to verify the improved model. The first one was to verify the ICDO. A large improvement was obtained in the solution accuracy and optimization stability, compared with particle swarm optimization (PSO), gray wolf optimization (GWO), firefly algorithm (FA), dung beetle optimizer (DBO), and subtraction average-based optimizer (SABO). The second one was to evaluate the quality of the atmospheric environment. Specifically, the atmospheric data was collected outside the South Subtropical Botanical Garden in Mazhang District, Zhanjiang City, Guangdong Province, China, from September 1, 2022, to September 30, 2023. The goodness of fit reached 0.999 for the prediction of atmospheric environmental quality, the mean square error was as low as 0.348, and the mean absolute percentage error was reduced to 0.729%. The third one was to classify the greenhouse environment. The data was collected in the greenhouses of the South Asian Tropical Botanical Garden. The accuracy rate of greenhouse environment classification was 99.21% with a precision rate of 99.91%. The data fusion was suitable for both indoor and outdoor environments, indicating better adaptability and high accuracy. This finding can also provide solid technical support to agricultural sensor data fusion in the field of precision agriculture.