PREDICTION AND REGULATION OF COTTON YIELD VIA CONVOLUTIONAL NEURAL NETWORK-BASED SOFTWARE SOLUTIONS
Keywords:
cotton yield prediction, precision agriculture, convolutional neural networks (CNN), pest detection, biostimulants, crop protection, yield optimizationAbstract
The research demonstrates that the combined application of biostimulants with chemical agents for pest and disease control significantly enhances cotton yield and fiber quality. Field experiments conducted in the Kashkadarya region of Uzbekistan showed that integrated treatment with Tandem biostimulant, Indoxacarb insecticide, and Zerox fungicide increased yield up to 39.2 c/ha in the Bukhoro-8 variety and 40.5 c/ha in the Marvarid variety, representing yield increases of 33.8% and 35% compared to control plots. The findings offer practical guidance for improving productivity and effective pest management in cotton farming through convolutional neural network-based software systems.References
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