The paper presents an intelligent control system for the indirect assessment of fruit damage volume based on the use of a computer vision system and a convolutional neural network (CNN). An algorithm has been developed that analyzes the surface defect area to predict the volume of damaged pulp. The proposed approach includes stages of image acquisition, preprocessing, defect segmentation using a CNN, regression-based damage volume estimation, and decision-making based on fuzzy logic. A mathematical model is described that links the defect area to the damage volume, taking into account the internal spread of rot within the fruit. The presented system enables prompt and objective quality control of fruits, contributing to the optimization of sorting, storage, and processing operations in the food industry and the agro-industrial sector.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production