Efficient Image Stitching in the Presence of Dynamic Objects and Structure Misalignment

This paper presents a new method for simultaneously eliminating visual artifacts caused by moving objects and structure misalignment in image stitching. Given that the input images are roughly aligned, our approach is implemented in two stages. In the first stage, we discover motions between input images, and then extract their corresponding regions through a multi-seed based region growing algorithm. In the second stage, with prior information provided by the extracted regions, we perform a graph cut optimization in gradient-domain to determine which pixels to use from each image to achieve seamless stitching. Our method is simple to implement and effective. The experimental results illustrate that the proposed approach can produce comparable or superior results in comparison with state-of-the-art methods.

Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate changes.To evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research groups.Nevertheless,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological features.Here,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery.To facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images.We trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified.Then,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice varieties.Through correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high accuracy.Hence,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

High-yield rice cultivation is an effective way to address the increasing food demand worldwide.Correct classification of high-yield rice is a key step of breeding.However,manual measurements within breeding programs are time consuming and have high cost and low throughput,which limit the application in large-scale field phenotyping.In this study,we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders.In total,13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield.Using an Unmanned Aerial Vehicle(UAV)platform equipped with a hyperspectral camera to capture images over multiple time series,a rice yield classification model based on the XGBoost algorithm was proposed.Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not.The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice.Thus,we developed a low-cost,high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.