Abstract
The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.
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Xu, L. (2009). STRAWBERRY MATURITY NEURAL NETWORK DETECTNG SYSTEM BASED ON GENETIC ALGORITHM. In: Li, D., Zhao, C. (eds) Computer and Computing Technologies in Agriculture II, Volume 2. CCTA 2008. IFIP Advances in Information and Communication Technology, vol 294. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0211-5_48
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