Abstract: Abstract: In the research field of agricultural crop growth state monitoring, it is difficult to capture an image that can meet the needed usage for a whole description of a crop's growth information. Image fusion could combine two or more source images into a single composite image with extended information, and it is very useful for a crop's monitoring system. Aiming at solving the fusion problem of crop images in different light conditions, a proper image fusion algorithm based on contourlet transformation (CT) theory was carried out. First, by using CT, all the source images were decomposed into multi-scale and multi-direction sub-bands; then, for the low frequency coefficients, linear weighted fusion rules were adopted to reduce the influence of noise. For the band-pass directional sub-band coefficients, a maximum gradient rule was used to meet the human visual characteristics. Finally, the inverse transformation of contourlet was used to get the fused image. To testify as to the performance of the algorithm, cucumbers, cherry tomatoes, eggplant, and peppers captured in different light conditions were used as experimental crops. The experiment was divided into two parts: (1) a performance comparison between CT and WT, (2) a comparison of different fusion results of CT. A comparative test by using WT method showed that the proposed method could get much better performance than wavelet based fusion methods and commonly used fusion rules. In particular, the MI of fused image was 27.04% higher than the wavelet based method, SF value increased by 37.73%, MSE parameter was 46.97% higher, Ent was also improved by 19.69%, CC had a little enhancement by 2.76%, and G' was 11.21% higher than the WT based image fusion algorithm. Also, PSNR value was boosted by an average 8.06%. Another comparative experiment with commonly used fusion rules showed that edge strong information was 0.30% higher, structure similarity was promoted by 0.50%, and the average gradient was boosted by 2.63% more than under "linear+max" rules, and entropy value was 5.07% higher than under "min+mean" rules. These experiments all showed that the proposed CT based image fusion algorithm was practical and valid for agricultural product image fusion in different light conditions. This research provides a useful reference for the fusion of crops in different conditions.