几张照片就能分析成熟度？AI挑瓜与人工挑瓜哪个更甜→

Core Viewpoint - The article investigates the effectiveness of AI in selecting ripe and sweet watermelons compared to traditional methods, highlighting the growing popularity of AI tools in agriculture and their limitations in sensory evaluation [1][10]. Group 1: AI Selection Process - AI tools can analyze watermelon images to determine ripeness and sweetness based on visual characteristics such as color, shape, and surface texture [11]. - Only one of the three AI models provided a clear recommendation for the sweetest watermelon, while the others were unable to make definitive selections [3][11]. - The AI's reliance on visual data means it cannot assess tactile or auditory cues, which are crucial for accurate selection [11]. Group 2: Traditional Selection Techniques - Traditional methods involve examining the watermelon’s stem and blossom end, weighing it, and tapping it to assess sound quality [5][8]. - A skilled worker demonstrated that experience allows for a more comprehensive evaluation, combining visual, tactile, and auditory information [5][8]. Group 3: Industry Implications - The rise of AI in watermelon selection reflects broader trends in smart agriculture, where technology enhances efficiency and quality control in farming practices [12][13]. - AI applications in agriculture include drone inspections and AI sorting equipment, which are becoming essential tools for modern farming [12][13]. - The integration of IoT, big data, and AI is transforming traditional agricultural practices, leading to improved productivity and precision [12][13].