## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Agricultural Product Quality Control and Food Safety
For example, a graded-line inspection model improved kiwifruit defect detection by considering multi-angle views and spatial distribution [283]. _Real-Time Monitoring and Combating Food Adulteration._ The real-time monitoring of product quality during harvesting is gaining traction. Robots enabled with \"finger vision\" can assess fruit surface conditions before detachment, preventing damaged or diseased products from entering the supply chain [150]. Density estimation methods complement object detection models like YOLO to evaluate fruit clustering and packing density [128]. Food adulteration detection has seen advancements, with deep learning models identifying adulterants in powdered spices like red chilli [284]. Hyperspectral imaging combined with CNN classification has been used for high-confidence predictions of surface anomalies in Achacha fruit [285]. _Innovations in Sensing and Grading Systems._ Recent innovations include mobile-integrated fluorescent sensors for detecting trace elements in food and water [286]. Electrospun membranes embedded with AuAg nanoclusters have demonstrated high selectivity and sensitivity for heavy metals and amino acids [286]. Near-infrared hyperspectral imaging has yielded reliable predictions of physicochemical attributes in durian pulp, such as firmness and sugar content [287]. Fruit grading systems continue to evolve with improvements in model architecture and training data quality. A multidimensional feature extraction network incorporating transformer modules has enhanced detection rates in complex backgrounds [288]. Multi-view image processing has improved visual evaluation consistency in apple grading systems by minimising occlusion effects [289]. _Future Directions._ The integration of regression-based GANs with hyperspectral imaging offers a promising pathway for real-time chemical safety monitoring [265]. Automated fruit quality inspection systems utilising transfer learning and GANs are being developed to maximise performance with minimal labelled data [290]. Specular highlight removal algorithms and denoising preprocessing techniques are increasingly integrated into CV pipelines to enhance inspection accuracy under varying lighting conditions [291]. When combined with lightweight detection architectures, these improvements facilitate deployment in resource-constrained environments. Mobile-based systems and edge AI solutions are transforming food quality monitoring. Deep learning models optimised for mobile deployment, such as those integrating CNNs with real-time classification pipelines, enable immediate, reliable quality assessments [292]. Smartphone-based colourimetric sensor arrays and spectrometers are increasingly used in field testing and supply chain quality management [273; 286]. In conclusion, CV and GenAI are reshaping agricultural product quality control and food safety, making inspections more accurate, scalable, and accessible. The synergy between sensing hardware, CV models, and GenAI ensures an intelligent and automated pipeline from field to consumer, safeguarding public health and fostering transparency across the food supply chain. Table 6 summarises key representative applications of CV and GenAI in agricultural quality control and food safety, highlighting specific methodologies and examples.