Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

中文翻译：

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使用深度卷积神经网络基于图像的水稻营养缺乏症诊断。

水稻植株营养缺乏的症状通常出现在叶子上。因此，叶片的颜色和形状可用于诊断水稻中的营养缺乏症。图像分类是用于此诊断任务的有效且快速的方法。深度卷积神经网络（DCNN）已被证明在图像分类中是有效的，但是将其用于识别水稻中的营养缺乏症却很少受到关注。在本研究中，我们探索了不同DCNNs诊断水稻营养缺乏症的准确性。通过水培实验获得了总计1818张植物叶子的照片，涵盖了全部营养和10类营养缺乏症。将照片以3：1：1的比例分为训练集，验证集和测试集。进行了微调，以评估四种最新的DCNN：Inception-v3，具有50层的ResNet，具有NasNet-Large的NasNet和具有121层的DenseNet。所有DCNN都获得了超过90％的验证和测试精度，其中DenseNet121表现最佳（验证精度= 98.62±0.57％；测试精度= 97.44±0.57％）。通过与支持向量机的颜色特征比较和支持向量机的定向梯度直方图比较，验证了DCNN的性能。这项研究表明，DCNNs为诊断水稻中的营养缺乏提供了一种有效的方法。通过与支持向量机的颜色特征比较和支持向量机的定向梯度直方图比较，验证了DCNN的性能。这项研究表明，DCNNs为诊断水稻中的营养缺乏提供了一种有效的方法。通过与支持向量机的颜色特征比较和支持向量机的定向梯度直方图比较，验证了DCNN的性能。这项研究表明，DCNNs为诊断水稻中的营养缺乏提供了一种有效的方法。