scholarly journals Monitoring the Spread of Water Hyacinth (Pontederia crassipes): Challenges and Future Developments

2021 ◽  
Vol 9 ◽  
Author(s):  
Aviraj Datta ◽  
Savitri Maharaj ◽  
G. Nagendra Prabhu ◽  
Deepayan Bhowmik ◽  
Armando Marino ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Hyacinth ◽  
Large Scale ◽  
Ecological Impacts ◽  
Automated Image Analysis ◽  
Machine Learning Techniques ◽  
Yield Potential ◽  
Effective Control ◽  
Weed Species ◽  
Invasive Weed

Water hyacinth (Pontederia crassipes, also referred to as Eichhornia crassipes) is one of the most invasive weed species in the world, causing significant adverse economic and ecological impacts, particularly in tropical and sub-tropical regions. Large scale real-time monitoring of areas of chronic infestation is critical to formulate effective control strategies for this fast spreading weed species. Assessment of revenue generation potential of the harvested water hyacinth biomass also requires enhanced understanding to estimate the biomass yield potential for a given water body. Modern remote sensing technologies can greatly enhance our capacity to understand, monitor, and estimate water hyacinth infestation within inland as well as coastal freshwater bodies. Readily available satellite imagery with high spectral, temporal, and spatial resolution, along with conventional and modern machine learning techniques for automated image analysis, can enable discrimination of water hyacinth infestation from other floating or submerged vegetation. Remote sensing can potentially be complemented with an array of other technology-based methods, including aerial surveys, ground-level sensors, and citizen science, to provide comprehensive, timely, and accurate monitoring. This review discusses the latest developments in the use of remote sensing and other technologies to monitor water hyacinth infestation, and proposes a novel, multi-modal approach that combines the strengths of the different methods.

scholarly journals Supporting Urban Weed Biosecurity Programs with Remote Sensing

2020 ◽  
Vol 12 (12) ◽  
pp. 2007
Author(s):  
Kathryn Sheffield ◽  
Tony Dugdale
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Data Availability ◽  
Surveillance Program ◽  
Urban Landscapes ◽  
Weed Species ◽  
Species Introduction ◽  
Weed Detection ◽  
Invasive Weed ◽  
Detection Rates

Weeds can impact many ecosystems, including natural, urban and agricultural environments. This paper discusses core weed biosecurity program concepts and considerations for urban and peri-urban areas from a remote sensing perspective and reviews the contribution of remote sensing to weed detection and management in these environments. Urban and peri-urban landscapes are typically heterogenous ecosystems with a variety of vectors for invasive weed species introduction and dispersal. This diversity requires agile systems to support landscape-scale detection and monitoring, while accommodating more site-specific management and eradication goals. The integration of remote sensing technologies within biosecurity programs presents an opportunity to improve weed detection rates, the timeliness of surveillance, distribution and monitoring data availability, and the cost-effectiveness of surveillance and eradication efforts. A framework (the Weed Aerial Surveillance Program) is presented to support a structured approach to integrating multiple remote sensing technologies into urban and peri-urban weed biosecurity and invasive species management efforts. It is designed to support the translation of remote sensing science into operational management outcomes and promote more effective use of remote sensing technologies within biosecurity programs.

scholarly journals Assessment of an Invasive Weed “Maimaio” Commelina foecunda in the Sesame Fields of Western Zone of Tigray, Northern Ethiopia

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
G. Zenawi ◽  
T. Goitom ◽  
B. Fiseha
Keyword(s):  
Large Scale ◽  
Growth Stage ◽  
Small Scale ◽  
Northern Ethiopia ◽  
Average Yield ◽  
Weed Species ◽  
Invasive Weed ◽  
Weed Infestation ◽  
Western Zone ◽  
Late Growth

Sesame (Sesamum indicum L.) is probably the most ancient oilseed. It has multiple uses; it is used as a source of food and in the pharmaceutical and cosmetics industries. The average yield of sesame in western Tigray is too low (about 400 kg/ha to 500 kg/ha) due to different factors, and weed infestation takes a lion’s share. More than 80 weed species were recorded and identified as weed pests for sesame in western Tigray. “Maimaio” Commelina foecunda is the most troublesome weed of sesame. The purpose of this study was to assess the distribution of C. foecunda and quantify its infestations. The survey was conducted in 24 sesame growing areas and 48 sesame farms from the three districts of western Tigray in the 2017 production season. The survey result showed that about 91.7% of the assessed sesame farms in western Tigray were found infested with C. foecunda. The weed frequently appeared in Kafta Humera. And, it occurred abundantly and closely in Kafta Humera, whereas it occurred poorly and irresolutely in Tsegede. Concentrated frequency, abundance, and density of the weed were recorded by large-scale sesame producers, lower growing altitudes, and early growth stage of sesame; whereas, it was limited in the small-scale farms, higher growing altitudes, and late growth stage of sesame.

scholarly journals YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data

2020 ◽  
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Lizhi Wang
Keyword(s):  
Remote Sensing ◽  
Continuous Monitoring ◽  
Large Scale ◽  
State Of The Art ◽  
Remote Sensing Data ◽  
Yield Potential ◽  
Soybean Yield ◽  
Growth State ◽  
Sensing Data ◽  
Learning Framework

AbstractLarge scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crop’s yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.

scholarly journals Accuracy and Efficiency Comparison of Object Detection Open-Source Models

2021 ◽  
Vol 17 (05) ◽  
pp. 165
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Khalid Rahmani
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Large Scale ◽  
Performance Comparison ◽  
Yield Potential ◽  
Temperate Climate ◽  
Weed Species ◽  
Weed Detection ◽  
Cloudy Weather

In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language.

scholarly journals IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 77-81
Author(s):  
M. Amani ◽  
A. Ghorbanian ◽  
S. Mahdavi ◽  
A. Mohammadzadeh
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Land Cover Mapping ◽  
Landsat 8

Abstract. Land cover classification is important for various environmental assessments. The opportunity of imaging the Earth’s surface makes remote sensing techniques efficient approaches for land cover classification. The only country-wide land cover map of Iran was produced by the Iranian Space Agency (ISA) using low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and a basic classification method. Thus, it is necessary to produce a more accurate map using advanced remote sensing and machine learning techniques. In this study, multi-temporal Landsat-8 data (1,321 images) were inserted into a Random Forest (RF) algorithm to classify the land cover of the entire country into 13 categories. To this end, all steps, including pre-processing, classification, and accuracy assessment were implemented in the Google Earth Engine (GEE) platform. The overall classification accuracy and Kappa Coefficient obtained from the Iran-wide map were 74% and 0.71, respectively, indicating the high potential of the proposed method for large-scale land cover mapping.

scholarly journals Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques

2020 ◽  
Vol 12 (7) ◽  
pp. 1200 ◽  
Author(s):  
Sunmin Lee ◽  
Yunjung Hyun ◽  
Saro Lee ◽  
Moung-Jin Lee
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Influential Factor ◽  
Machine Learning Techniques ◽  
Yield Potential ◽  
Control Factors ◽  
Learning Techniques ◽  
Groundwater Yield

Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.

scholarly journals Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Lizhi Wang
Keyword(s):  
Remote Sensing ◽  
Transfer Learning ◽  
Large Scale ◽  
Crop Yields ◽  
Remote Sensing Data ◽  
The United States ◽  
Growth Cycle ◽  
Yield Potential ◽  
Soybean Yield ◽  
Sensing Data

AbstractLarge-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1132 counties for corn and 1076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with an MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.

scholarly journals AN OPEN-SOURCE CANOPY CLASSIFICATION SYSTEM USING MACHINE-LEARNING TECHNIQUES WITHIN A PYTHON FRAMEWORK

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 175-182
Author(s):  
O. Smith ◽  
H. Cho
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Classification System ◽  
Large Scale ◽  
Tree Canopy ◽  
Machine Learning Techniques ◽  
Remotely Sensed Data ◽  
High Resolution Imagery ◽  
Learning Techniques ◽  
Cross Platform

Abstract. Studying deforestation has been an important topic in forestry research. Especially, canopy classification using remotely sensed data plays an essential role in monitoring tree canopy on a large scale. As remote sensing technologies advance, the quality and resolution of satellite imagery have significantly improved. Oftentimes, leveraging high-resolution imagery such as the National Agriculture Imagery Program (NAIP) imagery requires proprietary software. However, the lack of insight into the inner workings of such software and the inability of modifying its code lead many researchers towards open-source solutions. In this research, we introduce CanoClass, an open-source cross-platform canopy classification system written in Python. CanoClass utilizes the Random Forest and Extra Trees algorithms provided by scikit-learn to classify canopy using remote sensing imagery. Based on our benchmark tests, this new canopy classification system was 283 % to 464 % faster than commercial Feature Analyst, but it produced comparable results with a similarity of 87.56 % to 87.62 %.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Effect of Herbicides on the Management of the Invasive Weed Solanum rostratum Dunal (Solanaceae)

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 284
Author(s):  
Jackline Abu-Nassar ◽  
Maor Matzrafi
Keyword(s):  
Growth Stage ◽  
Late Summer ◽  
Early Spring ◽  
Growth Stages ◽  
Weed Species ◽  
Invasive Weed ◽  
Solanum Rostratum ◽  
Stage 4 ◽  
Chemical Solutions ◽  
Late Growth

Solanum rostratum Dunal is an invasive weed species that invaded Israel in the 1950s. The weed appears in several germination flashes, from early spring until late summer. Recently, an increase in its distribution range was observed, alongside the identification of new populations in the northern part of Israel. This study aimed to investigate the efficacy of herbicide application for the control of S. rostratum using two field populations originated from the Golan Heights and the Jezreel Valley. While minor differences in herbicide efficacy were recorded between populations, plant growth stage had a significant effect on herbicide response. Carfentrazone-ethyl was found to be highly effective in controlling plants at both early and late growth stages. Metribuzin, oxadiazon, oxyfluorfen and tembutrione showed reduced efficacy when applied at later growth stage (8–9 cm height), as compared to the application at an early growth stage (4–5 cm height). Tank mixes of oxadiazon and oxyfluorfen with different concentrations of surfactant improved later growth stage plant control. Taken together, our study highlights several herbicides that can improve weed control and may be used as chemical solutions alongside diversified crop rotation options. Thus, they may aid in preventing the spread and further buildup of S. rostratum field populations.

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