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What is Deep Learning for Tomato Nutrient Deficiency about?

This document discusses a comparative study using deep CNN models to forecast and classify macronutrient deficiencies in tomato plants. The study uses two deep learning models - Inception-ResNet v2 and Autoencoder - to classify images of tomato plants under different nutrient deficiency conditions. An ensemble averaging technique is also used to increase the predictive accuracy of the models. The models are tested on images of tomato leaves and fruits under calcium, potassium and nitrogen deficiencies. The

Author

majestic gyu

Language

EN