水在番茄(SolanumlycopersicumL.)的生长中起着非常重要的作用,而如何检测番茄的水分状况是精准灌溉的关键。本研究的目的是通过融合RGB来检测番茄的水分状况,通过深度学习的NIR和深度图像信息。设定了五个灌溉水平,以在不同的水状态下种植西红柿,灌溉量为150%,125%,100%,75%,通过修正的Penman-Monteith方程计算的参考蒸散量的50%,分别。西红柿的水分状况分为五类:严重灌溉赤字,略有灌溉赤字,适度灌溉,稍微过度灌溉,严重过度灌溉。RGB图像,取番茄植株上部的深度图像和近红外图像作为数据集。数据集用于训练和测试使用单模式和多模式深度学习网络构建的番茄水分状态检测模型,分别。在单模式深度学习网络中,两个CNN,VGG-16和Resnet-50在单个RGB图像上进行了训练,深度图像,或NIR图像共6例。在多模式深度学习网络中,两个或更多的RGB图像,深度图像和近红外图像分别用VGG-16或Resnet-50训练,共20种组合。结果表明,基于单模式深度学习的番茄水分状态检测准确率为88.97%~93.09%,而基于多模态深度学习的番茄水分状态检测准确率为93.09%~99.18%。多模态深度学习显著优于单模态深度学习。使用多模式深度学习网络建立的番茄水分状态检测模型具有RGB图像的ResNet-50和深度和NIR图像的VGG-16。该研究为番茄水分状态的无损检测提供了一种新方法,为精准灌溉管理提供了参考。
Water plays a very important role in the growth of tomato (Solanum lycopersicum L.), and how to detect the water status of tomato is the key to precise irrigation. The objective of this
study is to detect the water status of tomato by fusing RGB, NIR and depth image information through deep learning. Five irrigation levels were set to cultivate tomatoes in different water states, with irrigation amounts of 150%, 125%, 100%, 75%, and 50% of reference evapotranspiration calculated by a modified Penman-Monteith equation, respectively. The water status of tomatoes was divided into five categories: severely irrigated deficit, slightly irrigated deficit, moderately irrigated, slightly over-irrigated, and severely over-irrigated. RGB images, depth images and NIR images of the upper part of the tomato plant were taken as data sets. The data sets were used to train and test the tomato water status detection models built with single-mode and multimodal deep learning networks, respectively. In the single-mode deep learning network, two CNNs, VGG-16 and Resnet-50, were trained on a single RGB image, a depth image, or a NIR image for a total of six cases. In the multimodal deep learning network, two or more of the RGB images, depth images and NIR images were trained with VGG-16 or Resnet-50, respectively, for a total of 20 combinations. Results showed that the accuracy of tomato water status detection based on single-mode deep learning ranged from 88.97% to 93.09%, while the accuracy of tomato water status detection based on multimodal deep learning ranged from 93.09% to 99.18%. The multimodal deep learning significantly outperformed the single-modal deep learning. The tomato water status detection model built using a multimodal deep learning network with ResNet-50 for RGB images and VGG-16 for depth and NIR images was optimal. This
study provides a novel method for non-destructive detection of water status of tomato and gives a reference for precise irrigation management.