The application of FCM-based computer image segmentation technology in agricultural production

Traditional pest and disease detection methods mainly rely on manual observation and diagnosis, which is not only inefficient but also prone to errors. Therefore, studying an efficient and accurate computer image segmentation technology is of great significance for agricultural production. Therefore, this study employs the fuzzy C-means clustering algorithm and support vector machine to segment images in agricultural production through computer image segmentation technology, with the aim of solving issues in agricultural production. Based on the collected image dataset of the same crop with different diseases and degrees of disease, experiments are conducted. The results show that the average hue of the sample is between 0.0015 and 0.2613, and the average saturation is between 0.0136 and 0.6513. The histogram of pest and disease image segmentation in the research algorithm is relatively dense, indicating that the research algorithm has advantages in crop pest and disease image segmentation. Under different numbers of images, the regional consistency of different diseases is not significant, with an average consistency of over 8.0. Among the disease prediction accuracy, the highest is black spot disease, with a prediction accuracy of 96.3%. This indicates that the technology studied can effectively extract the characteristics of crop diseases and pests, improving the accuracy and efficiency of disease and pest detection. Meanwhile, this technology can also provide technical support for other applications in agricultural production, such as precision agriculture and intelligent agriculture.