scholarly journals Canopy Volume as a Tool for Early Detection of Plant Drought and Fertilization Stress: Banana plant fine-phenotype

2021 ◽  
Author(s):  
Anna Brook ◽  
Yasmin Tal ◽  
Oshry Markovich ◽  
Nataliya Rybnikova
Keyword(s):  
Precision Agriculture ◽  
Plant Traits ◽  
Advanced Technology ◽  
Plant Phenotyping ◽  
Growth Stages ◽  
Three Dimension ◽  
Banana Plant ◽  
Early Stages ◽  
Canopy Volume ◽  
Final Yield

AbstractIrrigation and fertilization stress in plants are limitations for securing global food production. Sustainable agriculture is at the heart of global goals because threats of a rapidly growing population and climate changes are affecting agricultural productivity. Plant phenotyping is defined as evaluating plant traits. Traditionally, this measurement is performed manually but with advanced technology and analysis, these traits can be observed automatically and nondestructively. A high correlation between plant traits, growth, biomass, and final yield has been found. From the early stages of plant development, lack of irrigation and fertilization directly influence developing stages, thus the final crop yield is significantly reduced. In order to evaluate drought and fertilization stress, plant height, as a morphological trait, is the most common one used in precision-agriculture research. The present study shows that three-dimension volumetric approaches are more representative markers for alerting growers to the early stages of stress in young banana plants’ for fine-scale phenotyping. This research demonstrates two different group conditions: 1) Normal conditions; and 2) zero irrigation and zero fertilization. The statistical analysis results show a successfully distinguished early stress with the volumetric traits providing new insights on identifying the key phenotypes and growth stages influenced by drought stress.

scholarly journals UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 915
Author(s):  
Farrah Melissa Muharam ◽  
Khairudin Nurulhuda ◽  
Zed Zulkafli ◽  
Mohamad Arif Tarmizi ◽  
Asniyani Nur Haidar Abdullah ◽  
...  
Keyword(s):  
Random Forest ◽  
Ensemble Learning ◽  
Rice Plant ◽  
Vegetation Index ◽  
Plant Traits ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Growth Stages ◽  
Learning Approach ◽  
Red Edge

Rapid, accurate and inexpensive methods are required to analyze plant traits throughout all crop growth stages for plant phenotyping. Few studies have comprehensively evaluated plant traits from multispectral cameras onboard UAV platforms. Additionally, machine learning algorithms tend to over- or underfit data and limited attention has been paid to optimizing their performance through an ensemble learning approach. This study aims to (1) comprehensively evaluate twelve rice plant traits estimated from aerial unmanned vehicle (UAV)-based multispectral images and (2) introduce Random Forest AdaBoost (RFA) algorithms as an optimization approach for estimating plant traits. The approach was tested based on a farmer’s field in Terengganu, Malaysia, for the off-season from February to June 2018, involving five rice cultivars and three nitrogen (N) rates. Four bands, thirteen indices and Random Forest-AdaBoost (RFA) regression models were evaluated against the twelve plant traits according to the growth stages. Among the plant traits, plant height, green leaf and storage organ biomass, and foliar nitrogen (N) content were estimated well, with a coefficient of determination (R2) above 0.80. In comparing the bands and indices, red, Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Red-Edge Wide Dynamic Range Vegetation Index (REWDRVI) and Red-Edge Soil Adjusted Vegetation Index (RESAVI) were remarkable in estimating all plant traits at tillering, booting and milking stages with R2 values ranging from 0.80–0.99 and root mean square error (RMSE) values ranging from 0.04–0.22. Milking was found to be the best growth stage to conduct estimations of plant traits. In summary, our findings demonstrate that an ensemble learning approach can improve the accuracy as well as reduce under/overfitting in plant phenotyping algorithms.

scholarly journals High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 258 ◽  
Author(s):  
Aakash Chawade ◽  
Joost van Ham ◽  
Hanna Blomquist ◽  
Oscar Bagge ◽  
Erik Alexandersson ◽  
...  
Keyword(s):  
Plant Breeding ◽  
High Throughput ◽  
Precision Agriculture ◽  
Plant Traits ◽  
Early Stage ◽  
Plant Phenotyping ◽  
Plant Cultivation ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Major Attention

High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.

scholarly journals Field assessment of a pulse width modulation (PWM) spray system applying different spray volumes: duty cycle and forward speed effects on vines spray coverage

2021 ◽  
Author(s):  
Marco Grella ◽  
Fabrizio Gioelli ◽  
Paolo Marucco ◽  
Ingrid Zwertvaegher ◽  
Eric Mozzanini ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Pulse Width ◽  
Pulse Width Modulation ◽  
Plant Protection ◽  
Plant Protection Products ◽  
Field Conditions ◽  
Advanced Technology ◽  
Real Field ◽  
Spray Application ◽  
Spray System

AbstractThe pulse width modulation (PWM) spray system is the most advanced technology to obtain variable rate spray application without varying the operative sprayer parameters (e.g. spray pressure, nozzle size). According to the precision agriculture principles, PWM is the prime technology that allows to spray the required amount where needed without varying the droplet size spectra which benefits both the uniformity of spray quality and the spray drift reduction. However, some concerns related to the effect of on–off solenoid valves and the alternating on/off action of adjacent nozzles on final uneven spray coverage (SC) have arisen. Further evaluations of PWM systems used for spraying 3D crops under field conditions are welcomed. A tower-shaped airblast sprayer equipped with a PWM was tested in a vineyard. Twelve configurations, combining duty cycles (DC: 30, 50, 70, 100%) and forward speeds (FS: 4, 6, 8 km h−1), were tested. Two methodologies, namely field-standardized and real field conditions, were adopted to evaluate the effect of DC and FS on (1) SC variability (CV%) along both the sprayer travel direction and the vertical spray profile using long water sensitive papers (WSP), and (2) SC uniformity (IU, index value) within the canopy at different depths and heights, respectively. Furthermore, the SC (%) and deposit density (Nst, no stains cm−2), determined using short WSP, were used to evaluate the spray application performances taking into account the spray volumes applied. Under field-controlled conditions, the pulsing of the PWM system affects both the SC variability measured along the sprayer travel direction and along the vertical spray profile. In contrast, under real field conditions, the PWM system does not affect the uniformity of SC measured within the canopy. The relationship between SC and Nst allowed identification of the ranges of 200–250 and 300–370 l ha−1 as the most suitable spray volumes to be applied for insecticide and fungicide plant protection products, respectively.

scholarly journals Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Crop Growth ◽  
Sensor System ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
The Internet ◽  
Growth Stages ◽  
Crop Breeding ◽  
Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

scholarly journals Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

2020 ◽  
Vol 12 (7) ◽  
pp. 1139
Author(s):  
Rui Dong ◽  
Yuxin Miao ◽  
Xinbing Wang ◽  
Zhichao Chen ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Fluorescence Sensor ◽  
Accurate Estimation ◽  
Growth Stages ◽  
Fluorescence Sensors ◽  
N Application ◽  
N Concentration ◽  
Leaf N Concentration ◽  
N Status ◽  
Leaf N

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

scholarly journals RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

2020 ◽  
Vol 12 (18) ◽  
pp. 2982 ◽  
Author(s):  
Christelle Gée ◽  
Emmanuel Denimal
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Weed Management ◽  
Vegetation Index ◽  
Management Strategies ◽  
Weed Competition ◽  
Growth Stages ◽  
Area Index ◽  
Crop Weed Competition ◽  
The Impact

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

scholarly journals Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6569 ◽  
Author(s):  
Adel H. Elmetwalli ◽  
Salah El-Hendawy ◽  
Nasser Al-Suhaibani ◽  
Majed Alotaibi ◽  
Muhammad Usman Tahir ◽  
...  
Keyword(s):  
Vegetative Growth ◽  
Growth Stage ◽  
Plant Traits ◽  
Growth Traits ◽  
Water Status ◽  
Accurate Estimation ◽  
Growth Stages ◽  
Irrigation Level ◽  
Arid Conditions ◽  
Irrigation Regimes

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.

scholarly journals Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook

2020 ◽  
Vol 12 (9) ◽  
pp. 1491 ◽  
Author(s):  
Gaetano Messina ◽  
Giuseppe Modica
Keyword(s):  
Precision Agriculture ◽  
State Of The Art ◽  
Plant Stress ◽  
Stress Conditions ◽  
Plant Diseases ◽  
Basic Knowledge ◽  
Plant Phenotyping ◽  
Future Research ◽  
Thermal Data ◽  
Low Altitude

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.

scholarly journals Estimating Biomass of Black Oat Using UAV-Based RGB Imaging

Agronomy ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 344 ◽  
Author(s):  
Matheus Gabriel Acorsi ◽  
Fabiani das Dores Abati Miranda ◽  
Maurício Martello ◽  
Danrley Antonio Smaniotto ◽  
Laercio Ricardo Sartor
Keyword(s):  
Cover Crops ◽  
Precision Agriculture ◽  
Temporal Variability ◽  
Prediction Models ◽  
Plant Biomass ◽  
Digital Terrain Model ◽  
Field Measurements ◽  
Growth Stages ◽  
Spatial And Temporal Variability ◽  
Growth Variability

The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.

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