Application of Internet of Things to Agriculture—The LQ-FieldPheno Platform:A High-Throughput Platform for Obtaining Crop Phenotypes in Field

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation.However,high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology.In view of this,this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno,which was developed by China National Agricultural Information Engineering Technology Research Centre.The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform,which introduces the Internet of Things(IoT)into agricultural breeding.The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment,including an automatic control system,upward field track,intelligent navigation vehicle,and environmental sensors.Furthermore,it combines an RGB camera,a 6-band multispectral camera,a thermal infrared camera,a 3-dimensional laser radar,and a deep camera.Special software is developed to control motions and sensors and to design run lines.Using wireless sensor networks and mobile communication wireless networks of IoT,the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput,automatic,and high time sequence.Moreover,the LQ-FieldPheno has the characteristics of multiple data acquisition,vital timeliness,remarkable expansibility,high-cost performance,and flexible customization.The LQ-FieldPheno has been operated in the 2020 maize growing season,and the collected point cloud data are used to estimate the maize plant height.Compared with the traditional crop phenotypic measurement technology,the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters.The proposed platform could contribute to the research of crop phenotype,remote sensing,agronomy,and related disciplines.

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

The number of leaves at a given time is important to characterize plant growth and development.In this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images.The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning models.The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation techniques.Furthermore,the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips.The method is claimed to be self-supervised since no manual labeling is required for model training.The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems.The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.