番茄酸甜，营养价值高，可溶性固形物含量(SSC)是衡量番茄风味的重要指标。由于植物对氮的吸收和同化机制不同,外源供给不同形态的氮素会对生长产生不同的影响,发展,和番茄的生理代谢过程，从而影响番茄的风味。在本文中,利用高光谱成像技术结合神经网络预测模型对不同氮肥处理下番茄SSC进行预测。使用竞争性自适应重加权采样（CARS）和迭代保留信息变量（IRIV）来提取特征波长。根据特征波长，通过构建和优化的自定义卷积神经网络(CNN)模型建立番茄SSC的预测模型。结果表明，番茄的SSC与氮肥浓度呈负相关。对于用不同氮浓度处理的西红柿，CARS-CNN和IRIV-并行卷积神经网络（PCNN）的残差预测偏差（RPD）分别达到1.64和1.66，均超过1.6，表明模型预测效果良好。本研究为今后番茄品质的在线无损检测提供了技术支持。实际应用：CARS-CNN和IRIV-PCNN是最好的数据处理模型。四个定制的卷积神经网络用于预测建模。CNN模型提供比常规方法更准确的结果。
Tomato is sweet and sour with high nutritional value, and soluble solids content (SSC) is an important indicator of tomato flavor. Due to the different mechanisms of nitrogen uptake and assimilation in plants, exogenous supply of different forms of nitrogen will have different effects on the growth, development, and physiological metabolic processes of tomato, thus affecting the tomato flavor. In this paper, hyperspectral imaging (HSI) technique combined with neural network prediction model was used to predict SSC of tomato under different nitrogen treatments. Competitive adaptive reweighed sampling (CARS) and iterative retained information variable (IRIV) were used to extract the feature wavelengths. Based on the characteristic wavelength, the prediction models of tomato SSC are established by custom convolutional neural network (CNN) model that was constructed and optimized. The results showed that the SSC of tomato was negatively correlated with nitrogen fertilizer concentration. For tomatoes treated with different nitrogen concentrations, the residual predictive deviation (RPD) of CARS-CNN and IRIV-parallel convolutional neural networks (PCNN) reached 1.64 and 1.66, both more than 1.6, indicating good model prediction. This study provides technical support for future online nondestructive testing of tomato quality. PRACTICAL APPLICATION: The CARS-CNN and IRIV-PCNN were the best data processing model. Four customized convolutional neural networks were used for predictive modeling. The CNN model provides more accurate results than conventional methods.
