computer-vision-based-biomass-estimation-for-invasive-plants

Computer Vision-Based Biomass Estimation for Invasive Plants

We report on the detailed steps of a method to estimate the biomass of invasive plants based on UAV remote sensing and computer vision. To collect samples ...

Guangxi University

Carbon quantum dots co-doped with iron and nitrogen (Fe@NCDs) were synthesized by using Passiflora edulis Sims (P. edulis) as a precursor. The Fe@NCDs exhibit outstanding peroxidase-mimetic activity owing to successful doping with iron resulting in a behavior similar to that of iron porphyrins. In the presence of HO, the Fe@NCDs catalyze the oxidation of the peroxidase substrate 3,3',5,5'-tetramethylbenzidine (TMB) with a color change from colorless to blue. The blue oxidation product has a characteristic absorption peaking at 652 nm. A colorimetric assay was worked out for uric acid (UA) that measures the hydrogen peroxide produced during oxidation of UA by uricase. Response is linear in the 2-150 μM UA concentration range, and the limit of detection is 0.64 μM. The method was applied to the determination of UA in (spiked) urine, and recoveries ranged from 92.0 to 103.4%. Graphical abstract Schematic representation of the fabrication of iron and nitrogen co-doped carbon dots (Fe@NCDs) using Passiflora edulis Sims as carbon-based materials. First, uric acid (UA) was oxidized to generate HO by uricase. Then, the Fe@NCDs catalyzed the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to form blue-colored oxidized TMB (oxTMB) in the presence of HO. UA can be quantified based on the theory.

Synthesis of carbon quantum dots with iron and nitrogen from Passiflora edulis and their peroxidase-mimicking activity for colorimetric determination of uric acid.

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

Applications of UAS in Crop Biomass Monitoring: A Review.

Mile-a-minute weed ( Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify . It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of  were collected using hyperspectral imaging in the spectral range of 450-998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing  in the wild.

Yanzhou Li

College of Mechanical Engineering

Guangxi University

Computer Vision-Based Biomass Estimation for Invasive Plants

Yanzhou Li

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}College of Mechanical Engineering

Guangxi University

Computer Vision-Based Biomass Estimation for Invasive Plants

College of Mechanical Engineering