Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

<div>Hydroponic experiment and ground truth. From left to right: seedling preparation, image capture in the laboratory, symptom observation in the greenhouse (top), and ground truth of the experiment in 2017 and 2018 (bottom).</div>

Computational Intelligence and Neuroscience

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Figure 1

Figure 1: Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice 