Fruit quality enhancement plays a vital role in wild blueberry cultivation. Fruit quality decreased after mechanical harvesting due to plant detritus such as leaves and stems. Its use in various agricultural settings may be limited by a count of parameters, including the difficulty of extrapolating results to other field conditions and habitats and slight variations in yield forecast accuracy among models. This manuscript presents an innovative approach to enhancing wild blueberry quality using heterogeneous context-aware graph convolutional networks (WBRD-YE-HCAGCN) for accurate ripeness detection and yield estimation, along with a comprehensive evaluation of performance metrics. Initially, the input image is gathered from two different harvest seasons. Then, the collected image is given to pre-processing utilizing the Interaction-Aware Labeled Multi-Bernoulli Filter (IALMBF). The IALMBF is used for Remove Noise and Image Quality Enhancement. After that, the pre-processed output is fed to Classification using Heterogeneous Context-Aware Graph Convolutional Networks (HCAGCN), which is used for detecting fruit maturity stage and yield estimation in wild blueberries. The effectiveness of the proposed method has been implemented in Python and evaluated using performance metrics such as accuracy, precision, recall, F1-score, MAE, and R2. The proposed model achieved excellent results: 97.5% accuracy, 1.135 seconds processing time, 98.2% recall, and a mean absolute error of 75.5. These results are compared with existing methods. Deep learning convolutional neural networks (DFMS-YEWB-CNN) are employed to detect the fruit maturity stage and estimate yield in wild blueberries. Additionally, cameras and image distance are evaluated for CNN-driven weed detection in wild blueberries (CID-WDWB-CNN). Ensemble machine learning techniques using computer-simulated images are also used to predict wild blueberry yield (CSD-WBYP-GBR).