Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Smart agriculture is the application of modern information and communication technologies (ICT) to agriculture, leading what we might call a third green revolution. These include object detection classification such as plants, leaves, weeds, fruits well animals pests in agricultural domain. Object detection, one most fundamental difficult issues computer vision has attracted lot attention lately. Its evolution over previous two decades can be seen pinnacle advancement. The objects done via digital image processing. Machine learning achieved significant advances field processing current years, significantly outperforming techniques. One techniques that popular Few-Shot Learning (FSL). FSL type meta-learning which learner given practice on several related tasks during meta-training phase able generalize successfully new but activities with limited number instances meta-testing phase. Here, smart particular reported. aim review state art currently available models, networks, classifications, offer some insights into possible future avenues research. It found shows higher accuracy 99.48% vegetable disease recognition dataset. also shown reliable use very few less training time.