Publication

Abstract : Accurate assessment of tomato (Solanum lycopersicum) ripeness is essential for the preservation of quality, meeting market demands and ensuring customer satisfaction. However, one of the key problems is accurately assessing the maturity levels of fruit under varying field conditions. Conventional computer vision models such as convolutional neural networks (CNN) demonstrate uneven performance under varying illumination conditions, particularly in arable farms. Further, it requires extensive training that involves fine-tuning entire model parameters and lags in global context learning. To address these issues, this work introduces a novel segmentation framework that integrates the SegFormer architecture with the Low-Rank Adaptation (SegLoRA) module. The proposed model attained significant performance improvement compared to state-of-the-art (SOTA) methods with a mean Intersection over Union (mIoU) of 83.25 %, an F1-score of 90.07 %, a test accuracy of 99.19 %, and a balanced accuracy of 93.88 %. Additionally, the computational cost was reduced by 26.98 % compared to existing SegFormer models. Further, the deployment on an edge computing device confirmed the proposed model’s feasibility in real time, with a minimal prediction delay of 0.065 s per frame. Moreover, its incorporation with an approximate yield estimation algorithm enables precise enumeration of harvestable tomatoes. These results demonstrate the scalability and efficiency of the SegLoRA, adding to the progress in automated ripeness detection and agricultural automation for selective harvesting operations.