Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat

Abstract

The present dissertation was motivated by the need to apply innovative technologies, automation, and artificial intelligence to agriculture in order to promote crop production while protecting our environment. The main objective of this dissertation was to develop sensor-based, automated frameworks for high-throughput phenotyping of wheat to identify advanced wheat varieties based on three desired traits, including yield potential, tolerance to salt stress (an abiotic stress), and resistance to Fusarium head blight disease (a biotic stress). We leveraged the advantages of hyperspectral imaging, a sophisticated sensing technology, and artificial intelligence including machine learning and deep learning algorithms. Through integrating imaging and high-resolution spectroscopy, hyperspectral imaging provides valuable insights into the internal activity of plants, leaf tissue structure, and physiological changes of plants in response to their environment. Alternatively, advanced machine learning and deep learning algorithms are uniquely suited to extract meaningful features and recognize latent patterns associated with the desired phenotyping trait, and ultimately make accurate inferences and prediction. In the first study (Chapter 2), we focused on salt stress phenotyping of wheat in a hydroponic system. A novel method was proposed for hyperspectral image analysis to assess the salt tolerance of four wheat varieties in a quantitative, interpretable, and don-invasive manner. The results of this study demonstrated the feasibility of quantitative ranking of salt tolerance in wheat varieties only one day after applying the salt treatment. In the second study (Chapter 3), we developed an ensemble feature selection pipeline by integrating six supervised feature selection techniques to identify the most informative spectral bands from high-dimensional hyperspectral images captured for plant phenotyping applications. First, the spectral features were ranked based on their ability to discriminate salt-stressed wheat plants from healthy plants at the earliest stages of stress. The proposed method could drastically reduce the dimension of hyperspectral images from 215 to 15 while improving the accuracy of classifying healthy and stressed vegetation pixels by 8.5%. Second, a clustering algorithm was proposed to form six broad spectral bands around the most prominent spectral features to aid in development of a multispectral camera. In the third study (Chapter 4), we aimed to develop a phenotyping framework for Fusarium head blight (FHB), a devastating disease attacking small grain crops. The most informative spectral bands were identified to detect FHB-infected spikes. The results of this study revealed that a set of two broad spectral bands (766 nm and 696 nm) returns a classification accuracy of 99% in detecting FHB-infected spikes. In the fourth study (Chapter 5), we developed an autonomous robotic framework for high-throughput yield phenotyping of wheat in the field. The data were collected by a hyperspectral camera mounted on an unmanned aerial vehicle flying over three experimental fields containing hundreds of wheat plots during two consecutive growing seasons. A deep neural network was trained to predict the yield of wheat plots and estimate the yield variation at a sub-plot scale. The coefficient of determination for predicting the yield at sub-plot and plot scale were 0.79 and 0.41with normalized root-mean-square error of 0.24 and 0.14, respectively. In the fifth study (Chapter 6), we focused on developing a deep autoencoder network by leveraging a large unlabeled dataset (~ 8 million pixels) to learn an optimal feature representation of hyperspectral images in a low dimensional feature space for yield prediction. The result demonstrated that the trained autoencoder could substantially reduce the dimension of hyperspectral images onto a 3-, 5-, and 10-dimenionsal feature space with a mean squared error less than 7e-5, while retaining the relevant information for yield prediction. At a higher level, this dissertation contributes to improving economic, ecological, and social impacts by improving crop production, reducing pesticides use, and properly leveraging salt-affected farmlands. From an environmental perspective, a cultivar with high yield potential and a cultivar resistant to FHB disease both promote sustainability in crop production and environment by reducing the required fertilizer and pesticide to meet the anticipated farmers’ profit. The intelligent, automated phenotyping frameworks developed in this dissertation can help plant scientists and breeders identify crop varieties with the desired traits tailored around promoting crop production and mitigating food security concerns.