Abstract: Abstract: Sitophilus oryzae is a weevil growing on diet of wheat grain. Its timely identification and control is essential to safeguarding wheat production. This paper proposes a computer vision-based method to diagnose its larval development inside wheat grain. After Sitophilus oryzae infects grains, its subsequent development is divided into egg stage, juvenile stage, elder stage, pupal stage and adult stage. We acquired a sequence of micro-CT projection images of the infested grains and then reconstructed the 2D images using the FDK algorithm. The larvae in the images were mapped out using segmentation and morphological method. Overall, we extracted 26 features to characterize a larva and its development, including morphological features, 3D features, invariant moment and texture features. The metamorphosis of Sitophilus oryzae was differentiated based on larval height, larval volume, its cross-sectional area, the minimum rectangle method, surficial area and perimeter of the cross section. The partial features simulated using the annealing algorithm composed of optimal features which were calculated by the fitness function, with the initial temperature T set at 150, drop rate at 0.9 and the end temperature at 1.0. Ten features were determined after 10 optimizations and the associated maximum fitness was 90.214 3%. The penalty factor c and the kernel function parameter g in the support vector machine (SVM) were optimized by the artificial bee colony (ABC) algorithm, in which the initial bee colony size was 20, the times of updates was set to be 50 and the maximum number of iterations was 50. Two parameters were optimized in the range of 0.01-100, and the algorithm was repeated twice to check robustness of the program. We used 250 images to train and test the model. The model correctly identified 97% of the larvae at different developmental stages when the parameters the penalty factor c=96.44, and the kernel function parameter g=0.01. The results showed that the height of Sitophilus oryzae larva had been in increase in the experiment; its volume, cross-sectional area, size of the minimum rectangle, surficial area and perimeter of cross-section had all asymptotically increased up to the pupal stage, followed by a decline after that. In addition, ABC-SVM correctly identified 97 images. The results presented in this paper indicated that computer vision can be used to identify larval development of Sitophilus oryzae in wheat grain.