Detection of Artificially Ripened Bananas Using Multispectral Images and Deep Learning

Identifying artificially ripened fruits, especially bananas, has become a crucial issue in today's modern culture because of growing worries about food safety. In addition to compromising the nutritional content and flavor of fruits, artificial ripening techniques, which frequently involve the use of dangerous chemicals like calcium carbide, also seriously endanger the health of the public. Even though current digital technologies provide significant improvements in agriculture and food processing, they often fail to distinguish between naturally ripened fruits and artificially ripened fruits, which could pose a risk to public health and result in financial losses for fruit growers. A ground-breaking answer to this urgent problem is offered by our initiative, which involves fusing cutting-edge computer analysis with sophisticated imaging techniques to transform the assessment of fruit ripening and guarantee consumer safety. State-of-the-art technologies, such as DJI Pro drones fitted with multispectral cameras for high-resolution imaging, are integrated into our methodology. With the help of this cutting-edge hardware and software combo, fruit ripening processes may be thoroughly monitored over vast agricultural landscapes, guaranteeing effective data collection and analysis. The usage of wavelength Signature graphs to spot unique patterns indicating the presence of heavy metals in calcium carbide in artificially ripened fruits is a crucial component of our research. Additionally, clustering algorithms are incorporated into our method to evaluate the consistency of chlorophyll content, a vital sign of organic ripening processes. The assessment of fruit ripening can be made more accurate and reliable by distinguishing between naturally and artificially ripened fruits based on physiological parameters through the analysis of spatial distribution patterns and intensity variations in chlorophyll levels. CNN is also used to produce supporting results for the classification of the fruit samples.