With the application of artificial intelligence in agriculture, the requirement of applying machine vision in the field to identify soil species has been raised. Different natural light will bring different images when soil images are collected by machine vision in the field, and it will affect soil species recognition. For refraining from this influence, one method is to collect completely images of soils under a variety of different natural lighting conditions. However, the acquisition of soil images in natural environments can be limited by natural conditions, time and economic costs, and it is difficult to implement. Thus, it may be an effective method that the soil image is converted to be similar to those real soil images that collected in the specific lighting environments, and it can eliminate the influence of inconsistent sunshine environments to improve the accuracy of soil species recognition. The main work of this paper is as follows.Multiple Gaussian fitting of brightness histogram of soil image is realized. Through studying and analyzing soil image, it is found that its Y component histogram is a skewness distribution and its left parts is similar to the left local area of a Gaussian curve, and the remainder that the Y component histogram is fitted by Gaussian curve still remains the features that its left parts is closed to the left local area of a Gaussian curve until the remainder becomes white noise. So the Y component histogram of a soil image can be fitted by several Gaussian curves. Based on the above ideas, an optimization model is established to fit the Gaussian curve of its left local area. Then, the fitting residual is computed and the next Gaussian fitting is executed until the fitting residual is small enough. The weighted fitting curve of multiple Gaussian fitting and weighted Gaussian subtractive fitting algorithm are obtained.Controllable brightness enhancement algorithm of soil image based on weighted Gaussian subtractive fitting is proposed. The target brightness is introduced into the weighted Gaussian subtraction fitting curve of an original soil image to calculate its probability density curve and cumulative distribution curve of the Y component of the expected enhanced image. The brightness migration is raised to realize the controllable brightness enhancement by the cumulative distribution curve of the original soil image and the cumulative distribution curve that the target brightness has poured into it. According to the principle of color ratio invariance, the transferred Y component carries out the color correction of U and V components to get the final controllable brightness enhancement of soil images.Simulation experiments prove that the algorithm can enhance soil image with controllable brightness and is effective. Three sample sets for simulation experiments are constructed and eight experiments are done to test the algorithm. We compare the proposed algorithm with the existing controllable brightness enhancement algorithms, such as 1-D HS and 2-D HS. The experiments include the converting sub-images from high to low brightness and the converting from low to high brightness in each pair of sub-images, and the results indicate that the proposed algorithm has less variation in brightness difference accuracy and color difference accuracy, higher controllable accuracy of brightness and less distortion. Next, experiments on the effective range of brightness adjustment are conducted. The brightness means of a sub-image as its brightness base point, and the step difference is 10 in the brightness increment experiment and the brightness decrement experiment. Simulation results exhibit that increasing or decreasing 30 brightness gray levels is the effective range of soil image brightness enhancement with the algorithm in this paper. Finally, a weighted Gaussian subtraction fitting experiment is performed, the results show that the algorithm can adaptively obtain the fitting times and algorithm convergence is implemented after the iteration is performed 6~8 times, and the fitting accuracy is improved.