ieee agriculture 2020-21 code title and abstract

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IEEE

2020-21

AGRICULTURE

CODE TITLE AND ABSTRACT

21ANSP_AC01 Automatic Recognition of Soybean Leaf Diseases Using UAV Images

and Deep Convolutional Neural Networks

Plant diseases are a crucial issue in agriculture. An accurate and

automatic identification of leaf diseases could help to develop an early

response to reduce economic losses. Recent research in plant diseases

has adopted deep neural networks. However, such research has used the

models as a black-box passing the labeled images through the networks.

This letter presents an analysis of the network weights for the automatic

recognition of soybean leaf diseases applied to images taken straight

from a small and cheap unmanned aerial vehicle (UAV). To achieve

high accuracy, we evaluated four deep neural network models trained

with different parameters for fine-tuning (FT) and transfer learning.

Data augmentation and dropout were used during the network training

to avoid overfitting. Our methodology consists of using the SLIC

method to segment the plant leaves in the top-view images obtained

during the flight. We tested our data set created from real flight

inspections in an end-to-end computer vision approach. Results strongly

suggest that the FT of parameters substantially improves the

identification accuracy.

21ANSP_AC02 Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation

Temperature Condition Index in the Guanzhong Plain, China

The vegetation temperature condition index (VTCI) has been shown to

perform well for drought monitoring using multiyear advanced very

high-resolution radiometer (AVHRR) and moderate resolution imaging

spectroradiometer (MODIS) data. Compared to single-year VTCI, the

VTCI calculated by multiyear data can quantitatively reflect the degree

of drought and precipitation in a region. Sentinel-3 is a recently

launched remote sensing satellite with high temporal resolution and is

similar to the satellites carrying AVHRR and MODIS sensors. One year

of Sentinel-3 data is available for calculating the VTCI, and given the

need for developing quantitatively drought monitoring capabilities, the

aim of this study is to investigate the methods used to calculate the

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potential multiyear Sentinel-3 VTCI for quantitative drought

monitoring. This is based on a comparison with multiyear Terra MODIS

VTCI and a correlation analysis with cumulative precipitation data. The

analysis results indicate that the potential multiyear Sentinel-3 VTCI

can be accurately calculated from the single-year Sentinel-3 VTCI based

on the linear correlation between the single-year VTCI and multiyear

VTCI derived from Terra MODIS, which do not exhibit obvious

systematic deviations from the multiyear Terra MODIS VTCI (the

absolute value of the average bias < 0.001). Therefore, it is proposed

that Sentinel-3 can successfully inherit the VTCI-based drought

monitoring tasks from MODIS.

21ANSP_AC03 CRowNet: Deep Network for Crop Row Detection in UAV Images

Nowadays, the development of robots and smart tractors for the

automation of sowing, harvesting, weeding etc. is transforming

agriculture. Farmers are moving from an agriculture where everything

is applied uniformly to a much more targeted farming. This new kind of

farming is commonly referred to as precision agriculture. However for

autonomous guidance of these agricultural machines and even

sometimes for weed detection an accurate detection of crop rows is

required. In this paper we propose a new method called CRowNet which

uses a convolutional neural network (CNN) and the Hough transform to

detect crop rows in images taken by an unmanned aerial vehicle (UAV).

The method consists of a model formed with SegNet (S-SegNet) and a

CNN based Hough transform (HoughCNet). The performance of the

proposed method was quantitatively compared to traditional approaches

and it showed the best and most robust result. A good crop row detection

rate of 93.58% was obtained with an IoU score per crop row above 70%.

Moreover the model trained on a given crop field is able to detect rows

in images of different types of crops.

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21ANSP_AC04 FB-CNN: Feature Fusion-Based Bilinear CNN for Classification of

Fruit Fly Image

The high-resolution devices for image capturing and the high

professional requirement for users, are very complex to extract features

of the fruit fly image for classification. Therefore, a bilinear CNN model

based on the mid-level and high-level feature fusion (FB-CNN) is

proposed for classifying the fruit fly image. At the first step, the images

of fruit fly are blurred by the Gaussian algorithm, and then the features

of the fruit fly images are extracted automatically by using CNN.

Afterward, the mid- and high-level features are selected to represent the

local and global features, respectively. Then, they are jointly

represented. When finished, the FB-CNN model was constructed to

complete the task of image classification of the fruit fly. Finally,

experiments data show that the FB-CNN model can effectively classify

four kinds of fruit fly images. The accuracy, precision, recall, and F1

score in testing dataset are 95.00%, respectively. 21ANSP_AC05 Passive Measurement Method of Tree Height and Crown Diameter

Using a Smartphone

The tree height and crown diameter are important measurement

attributes in forest resource survey and management. Hence, we propose

a passive measurement method of tree height and crown diameter based

on monocular camera of a smartphone. First, we use an feature-adaptive

Mean-Shift algorithm to segment the image and extract tree’s contour.

Furthermore, an adaptive feature coordinate system is established to

help study the conversion relationship of the coordinate systems. It has

been proved that for the image points with the same abscissa pixels, their

ordinate pixels have a linear relationship with its actual imaging angles.

A depth extraction model is built according to this principle. Then, we

obtain the rotation and translation matrix and established tree height and

crown diameter models according to the mapping transformation

relationship of coordinates. Experimental results reveal significant

correlation between calculated and truth values. The RMSE is 0.267 m

( rRMS=2.482%) for tree height and 0.209 m ( rRMS=5.631%) for

crown diameter. The relative errors of tree heights are less than 5.76%

(MRE=2.159%); for crown diameter, the relative errors are less than

9.73% (MRE=4.95%). Overall, the accuracy of this method falls within

the requirements of the continuous inventory of Chinese national forest

resources

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21ANSP_AC06 Performance Evaluation of Crop Segmentation Algorithms

This paper presents a thorough evaluation of twenty-one state-of-the-art

widely-used crop segmentation algorithms, motived by their

significance in vision tasks for further analysis. An ideal crop

segmentation algorithm can effectively extract crop information, thus

providing an important precondition for the application of intelligent

agriculture analytics. In order to enable researchers in this field to fully

understand various crop segmentation methods, this paper proposes a

new classification strategy of object segmentation by dividing the

algorithms into pixel-based and region-based approaches at first, and

then systematically evaluating various crop segmentation methods with

a unified data benchmark and four common metrics. A new dataset

which incorporates crop variety, environment condition and observation

distance into consideration is constructed for demonstrating the

experiments and comparisons. The effectiveness and robustness of these

algorithms were evaluated by three sets of comparative experiments.

Based on the quantitative results, we summarize the advantages and

disadvantages of the evaluated algorithms from the segmentation

performances with four metric indicators. Furthermore, the discussion

and evaluation results will provide great support for precision

agriculture analysis. 21ANSP_AC07 Analysis and Identification of Rice Adulteration Using Terahertz

Spectroscopy and Pattern Recognition Algorithms

Rice adulteration is a severe problem in agro-products and food

regulatory agencies, suppliers, and consumers. In this study, to

effectively distinguish whether high-quality rice is mixed with low-

quality rice, detection and analysis of adulterated rice in five levels with

different mixing proportions was conducted via terahertz spectroscopy

and pattern recognition algorithms. Initially, samples were prepared and

spectral data were acquired by using the terahertz transmission mode,

and a principal component analysis (PCA) algorithm was applied to

extract features from original spectrum information and reduce data

dimensions. Subsequently, partial least squares-discriminant analysis

(PLS-DA), support vector machine (SVM), and a back propagation

neural network (BPNN) combined with the absorption spectra after

different pretreatments, including standard normal variate (SNV)

transformation, baseline correction (BC), and first derivative (1st

derivative), were applied to establish the classification models. Results

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indicate that an SVM model employing the absorption spectra with a 1st

derivative pretreatment exhibits the best discrimination ability, with an

accuracy up to 97.33% in the prediction set. This result proves that

terahertz spectroscopy combined with chemometric methods can be an

effective tool to identify rice adulteration levels.

21ANSP_AC08 Security and Privacy for Green IoT-Based Agriculture: Review,

Blockchain Solutions, and Challenges

This paper presents research challenges on security and privacy issues

in the field of green IoT-based agriculture. We start by describing a four-

tier green IoT-based agriculture architecture and summarizing the

existing surveys that deal with smart agriculture. Then, we provide a

classification of threat models against green IoT-based agriculture into

five categories, including, attacks against privacy, authentication, and

confidentiality, availability, and integrity properties. Moreover, we

provide a taxonomy and a side-by-side comparison of the state-of-the-

art methods toward secure and privacy-preserving technologies for IoT

applications and how they will be adapted for green IoT-based

agriculture. In addition, we analyse the privacy-oriented block chain-

based solutions as well as consensus algorithms for IoT applications and

how they will be adapted for green IoT-based agriculture. Based on the

current survey, we highlight open research challenges and discuss

possible future research directions in the security and privacy of green

IoT-based agriculture. 21ANSP_AC09 Water Management in Agriculture: A Survey on Current Challenges

and Technological Solutions

Water plays a crucial role in the agricultural field for food production

and raising livestock. Given the current trends in world population

growth, the urgent food demand that must be answered by agriculture

highly depends on our ability to efficiently exploit the available water

resources. Among critical issues, there is water management. Recently,

innovative technologies have improved water management and

monitoring in agriculture. Internet of Things, Wireless Sensor Networks

and Cloud Computing, have been used in diverse contexts in agriculture.

By focusing on the water management challenge in general, existing

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approaches are aiming at optimizing water usage, and improving the

quality and quantity of agricultural crops, while minimizing the need for

direct human intervention. This is achieved by smoothing the water

monitoring process, by applying the right automation level, and

allowing farmers getting connected anywhere and anytime to their

farms. There are plenty of challenges in agriculture involving water:

water pollution monitoring, water reuse, monitoring water pipeline

distribution network for irrigation, drinking water for livestock, etc.

Several studies have been devoted to these questions in the recent

decade. Therefore, this paper presents a survey on recent works dealing

with water management and monitoring in agriculture, supported by

advanced technologies. It also discusses some open challenges based on

which relevant research directions can be drawn in the future, regarding

the use of modern smart concepts and tools for water management and

monitoring in the agriculture domain. 21ANSP_AC10 Precision Regulation Model of Water and Fertilizer for Alfalfa Based

on Agriculture Cyber-Physical System

The regulation of water and fertilizer for alfalfa growth is not precise

enough because the regulation strategy cannot track alfalfa growth

dynamically. In this paper, we propose a precision regulation model of

water and fertilizer for alfalfa based on agriculture cyber-physical

system (ACPS) for irrigation and fertilizer management in alfalfa

(PRMWFA-ACPS). The proposed PRMWFA-ACPS is a

comprehensive model that includes the biophysical submodel, the

computation submodel of water and fertilizer regulation, and the

interaction of the submodels for both. The proposed model interacts

with the alfalfa growth and its physical environment along with the

irrigation strategy to improve the precise regulation of water and

fertilizer. To verify the performance of the proposed model, we develop

a simulation platform for PRMWFA-ACPS based on Ptolemy. Through

physical experiments performed in the field at the Ningxia irrigation

area of the Yellow River over three years (2016-2018), we verified and

analyzed PRMWFA-ACPS by comparing the simulated and measured

values, such as the growth period, leaf area index, soil water content and

alfalfa yield. The experimental results show that the mean relative error

of the growth period simulated by the model is between 1.9% and 6.8%,

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the mean relative error of the leaf area index simulated by the model is

between 2.1% and 9.8%, the mean relative error of the soil water content

simulated by the model is between 4.3% and 12.8%, and the mean

relative error of the yield simulated by the model is between 1.2% and

14.3%. These findings indicate that PRMWFA-ACPS has promising

applicability to the Ningxia irrigation area of the Yellow River and

improves the accurate regulation of water and fertilizer application to

alfalfa in a complex physical environment.

21ANSP_AC11 System Assessment of WUSN Using NB-IoT UAV-Aided Networks in

Potato Crops

Unmanned Aerial Vehicles (UAV) are part of precision agriculture;

also, their impact on fast deployable wireless communication is offering

new solutions and systems never envisioned before such as collecting

information from underground sensors by using low power Internet of

Things (IoT) technologies. In this paper, we propose a (Narrow Band

IoT) NB-IoT system for collecting underground soil parameters in

potato crops using a UAV-aided network. To this end, a simulation tool

implementing a gateway mounted on a UAV using NB-IoT based access

network and LTE based backhaul network is developed. This tool

evaluates the performance of a realistic scenario in a potato field near

Bogota, Colombia, accounting for real size packets in a complete IoT

application. While computing the wireless link quality, it allocates

access and backhaul resources simultaneously based on the technologies

used. We compare the performance of wireless underground sensors

buried in dry and wet soils at four different depths. Results show that a

single drone with 50 seconds of flight time could satisfy more than 2000

sensors deployed in a 20 hectares field, depending on the buried depth

and soil characteristics. We found that an optimal flight altitude is

located between 60 m and 80 m for buried sensors. Moreover, we

establish that the water content reduces the maximum reachable buried

depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we

found that in the proposed scenario, sensors’ battery life could last up to

82 months for above ground sensors and 77 months for the deepest

buried ones. Finally, we discuss the influence of the sensor’s density

and buried depth, the flight service time and altitude in power-

constrained conditions and we propose optimal configuration to

improve system performance

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21ANSP_AC12 A Novel Distributed CDS Algorithm for Extending Lifetime of WSNs

With Solar Energy Harvester Nodes for Smart Agriculture Applications

Recent improvements in computer and software technologies affect

different areas of production systems. The productivity of an

agricultural process can be accepted as one of the many domains

affected by these improvements. The production volume is increased

not only with new designs of agricultural machines, but also by utilizing

communication technologies in the production process. Even the

wireless sensor technologies have started to be used for gathering some

environmental features in order to optimize production parameters as

well as preserving the product in determined quality levels. However,

nodes which play an important part of such systems are prone to battery

depletions. To alleviate this battery problem, incorporating harvester

nodes into the system has been recently considered. However, only

including such nodes is not sufficient for improving the lifetime of the

system. In this paper, a new distributed connected dominating set

algorithm on WSNs with solar energy harvester nodes for precision

agriculture applications is proposed. The novel distributed connected

dominating set construction with solar energy harvesting in smart

agriculture applications algorithm, namely CDSSEHA, is compared

with the traditional flooding methods and with an energy efficient CDS

algorithm. According to the results, the proposed algorithm increased

the WSNs’ lifetime by up to approximately 6 times and 1.4 times

compared to the traditional flooding methods and CDS based method,

respectively. Furthermore, the CDS construction process constitutes

only about 15% of the whole lifetime.

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21ANSP_AC13 Deep Neural Network-Based System for Autonomous Navigation in

Paddy Field

This paper presents a novel vision based approach for detecting rows of

crop in paddy field. The precise detection of crop row enables a farm-

tractor to autonomously navigate the field for successful inter-row

weeding. While prior works on crop row detection rely primarily on

various image based features, a deep neural network based approach for

learning semantic graphics to directly extract the crop rows from an

input image is used in this work. A deep convolutional encoder decoder

network is trained to detect the crop lines using semantic graphics. The

detected crop lines are then used to derive control signal for steering the

tractor autonomously in the field. The results demonstrate that the

proposed method is able to detect the rows of paddy accurately and

enable the tractor to navigate autonomously along the crop rows even

with a simple proportional only controller 21ANSP_AC14 Photovoltaic Agricultural Internet of Things Towards Realizing the

Next Generation of Smart Farming

Serious challenges for to drive agricultural sustainability combined with

climate crisis issues have induced an urgent need to decarbonise

agriculture. In this paper, we briefly introduce a novel concept of the

Photovoltaic Agricultural Internet of Things (PAIoT). This system

approach fuses agricultural production with renewable power

generation and control via an IoT platform. We discuss PAIoT

applications and potential to realize the next generation of smart

farming. In addition, we provide a review of key issues on the feasibility

of PAIoT and further propose novel techniques to mitigate these key

issues. 21ANSP_AC15 Crop Yield Estimation in the Canadian Prairies Using Terra/MODIS-

Derived Crop Metrics

We evaluated the utility of Terra/MODIS-derived crop metrics for yield

estimation across the Canadian Prairies. This study was undertaken at

the Census Agriculture Region (CAR) and the Rural Municipality (RM)

of the province of Saskatchewan, in three prairie agro-climate zones.

We compared MODIS-derived vegetation indices, gross primary

productivity (GPP), and net primary productivity (NPP) to the known

yields for barley, canola, and spring wheat. Multiple linear regressions

were used to assess the relationships between the metrics and yield at

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the CAR and RM levels for the years 2000 to 2016. Models were

evaluated using a leave-one-out cross validation (LOOCV) approach.

Results showed that vegetation indices at crop peak growing stages were

better predictors of yield than GPP or NPP, and EVI2 was better than

NDVI. Using seasonal maximum EVI2, CAR-level crop yields can be

estimated with a relative root-mean-square-error (RRMSE) of 14–20%

and a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.53–0.70,

though the exact relationship varies by crop type and agro-climate zone.

LOOCV showed the stability of the models across different years,

although interannual fluctuations of estimation accuracy were observed.

Assessments using RM-level yields showed slightly reduced accuracy,

with NSE of 0.37–0.66, and RRMSE of 18–28%. The best performing

models were used to map annual crop yields at the Soil Landscapes of

Canada (SLC) polygon level. The results indicated that the models could

perform well at both spatial scales, and thus, could be used to

disaggregate coarse resolution crop yields to finer spatial resolutions

using MODIS data.

21ANSP_AC16 Recognition of Weeds in Wheat Fields Based on the Fusion of RGB

Images and Depth Images

Due to the low recognition rate of weeds in wheat fields and the inability

to accurately locate weeds, we propose a recognition method for weeds

in natural wheat fields based on the fusion of RGB image features and

depth features. The method breaks through the limitations of the two-

dimensional spatial features extracted from RGB images when

recognizing grass weeds similar to wheat. According to the species,

distribution of weeds in wheat fields, we extracted the color, position,

texture, and depth features of weeds in wheat fields from RGB and depth

images during the tillering and jointing stages. And then used the

AdaBoost algorithm for the integrated learning of multiple classifiers,

thereby achieving the recognition of weeds in wheat fields. The

experimental results revealed that the recognition speed of weeds during

the tillering stage was 0.2 s and the accuracy rate was 88%. The

recognition speed of weeds during the jointing stage was 0.69 s, and the

accuracy rate of weed recognition was 81.08%. These results are

significantly higher than the weed recognition rate based on features

extracted from RGB images.

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21ANSP_AC17 A Multi-Modal Approach for Crop Health Mapping Using Low Altitude

Remote Sensing, Internet of Things (IoT) and Machine Learning

The agriculture sector holds paramount importance in Pakistan due to

the intrinsic agrarian nature of the economy. Pakistan has its GDP based

on agriculture, however it relies on manual monitoring of crops, which

is a labour intensive and ineffective method. In contrast to this, several

cutting edge technology based solutions are being employed in the

developed countries to enhance the crop yield with the optimal use of

resources. To this end, we have proposed an integrated approach for

monitoring crop health using IoT, machine learning and drone

technology. The integration of these sensing modalities generate

heterogeneous data which not only varies in nature (i.e. observed

parameter) but also has different temporal fidelity. The spatial

resolution of these methods is also different, hence, the optimal

integration of these sensing modalities and their implementation in

practice are addressed in the proposed system. In our proposed solution,

the IoT sensors provide the real-time status of environmental parameters

impacting the crop, and the drone platform provide the multispectral

data used for generating Vegetation Indices (VIs) such as Normalized

Difference vegetation Index (NDVI) for analyzing the crop health. The

NDVI provides information about the crop based on the chlorophyll

content, which offers limited information regarding the crop health. In

order to obtain a rich and detailed knowledge about crop health, the

variable length time series data of IoT sensors and multispectral images

were converted to a fixed-sized representation to generate crop health

maps. A number of machine and deep learning algorithms were applied

on the collected data wherein deep neural network with two hidden

layers was found to be the most optimal model among all the selected

models, providing an accuracy of (98.4%). Further, the health maps

were validated through ground surveys and by agriculture experts due

to the absence of reference data. The proposed research is basically an

indigenous, technology based agriculture solution capable of providing

important insights into the crop health by extracting complementary

features from multi-modal data set, and minimizing the crop ground

survey effort, particularly useful when the agriculture land is large in

size.

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21ANSP_AC18 Modeling Year-to-Year Variations of Clear-Sky Land Surface

Temperature Using Aqua/MODIS Data

Land surface temperature (LST) and its annual or inter-annual variations

play an important role in understanding global climate change, urban

heat island, and the process of land-atmosphere energy exchange. Many

annual temperature cycle (ATC) models [i.e., ATC with three or five

parameters (ACP3 or ACP5)] have been proposed to analyse the annual

variations of LST in the past decades. In this study, two year-to-year

continuous and derivable models (YYCD_ACP3 and YYCD_ACP5

models) were proposed to model several years of ATCs. The fitting

results of the YYCD_ACP3 model with global Aqua/MODIS daytime

LSTs from 2014 to 2018 show that the YYCD_ACP3 model achieved a

good performance in fitting the time-series LSTs with an overall

normalized root-mean-square error (NRMSE) of 0.21, coefficient of

determination (R2 ) of 0.74, and refined index of agreement (d) of 0.85.

In addition, the modeling results of ten representative samples covering

different climatic conditions and land cover worldwide show that,

except for two sites located in tropical and Antarctic, the YYCD_ACP3

model could show a good performance with R 2 greater than 0.6.

Although the ACP3 model shows similar performance to the

YYCD_ACP3 model, the fitting curve of the YYCD_ACP3 model is

continuous and smooth for describing the interannual variations of LST.

When the LSTs of 2014–2018 are fitted as a whole by using both

models, the YYCD_ACP3 model shows a slightly better performance

than that of the ACP3 model. The application of the YYCD_ACP3

model with the global MODIS LSTs from 2003 to 2018 indicates that

the results of the YYCD_ACP3 model have the potential to reveal the

interannual variations of LST. Therefore, we conclude that the YYCD

models are valuable for modeling the variations of LST over several

years and can be widely applied.

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21ANSP_AC19 CNN–SVM: a classification method for fruit fly image with the

complex background

On the basis of the problem that the image background is simple and the

traditional shooting equipment of fruit flies is too high, this study

improved the convolutional neural network model. First, the authors

changed Softmax classifier to support vector machine (SVM).

Moreover, then used convolution layers for extracting features of fruit

fly images. Finally, they fed features into SVM for training.

Experiments show that the model has been classifying the Bactrocera

dorsalis Hendel, Bactrocera cucurbitae, Bactrocera tau and Bactrocera

scutellata with accuracy over 92.04%, accordingly making the effective

classification of the complex background fruit fly images possible.

Moreover, it also provides a good practical application prospect.

21ANSP_AC20 An Intelligent IoT-Based System Design for Controlling and

Monitoring Greenhouse Temperature

The Kingdom of Saudi Arabia is known for its extreme climate where

temperatures can exceed 50 ◦C, especially in summer. Improving

agricultural production can only be achieved using innovative

environmentally suitable solutions and modern agricultural

technologies. Using Internet of Things (IoT) technologies in greenhouse

farming allows reduction of the immediate impact of external climatic

conditions. In this paper, a highly scalable intelligent system

controlling, and monitoring greenhouse temperature using IoT

technologies is introduced. The first objective of this system is to

monitor the greenhouse environment and control the internal

temperature to reduce consumed energy while maintaining good

conditions that improve productivity. A Petri Nets (PN) model is used

to achieve both monitoring of the greenhouse environment and

generating the suitable reference temperature which is sent later to a

temperature regulation block. The second objective is to provide an

Energy-Efficient (EE) scalable system design that handles massive

amounts of IoT big data captured from sensors using a dynamic graph

data model to be used for future analysis and prediction of production,

crop growth rate, energy consumption and other related issues. The

design tries to organize various possible unstructured formats of raw

data, collected from different kinds of IoT devices, unified and

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technology-independent fashion using the benefit of model

transformations and model-driven architecture to transform data in

structured form.

21ANSP_AC21 Time-Series of Sentinel-1 Interferometric Coherence and Backscatter

for Crop-Type Mapping

The potential use of the interferometric coherence measured with

Sentinel-1 satellites as input feature for crop classification is explored

in this study. A one-year time-series of Sentinel-1 images acquired over

an agricultural area in Spain, in which 17 crop species are present, is

exploited for this purpose. Different options regarding temporal

baselines, polarization, and combination with radiometric data

(backscattering coefficient) are analysed. Results show that both

radiometric and interferometric features provide notable classification

accuracy when used individually (overall accuracy lies between 70%

and 80%). It is found that the shortest temporal baseline coherences (6

days) and the use of all available intensity images perform best, hence

proving the advantage of the 6-day revisit time provided by the Sentinel-

1 constellation with respect to longer revisit times. It is also shown that

dual-pol data always provide better classification results than single-pol

ones. More importantly, when both coherence and backscattering

coefficient are jointly used, a significant increase in accuracy is obtained

(greater than 7% in overall accuracies). Individual accuracies of all crop

types are increased, and an overall accuracy above 86% is reached. This

proves that both features provide complementary information, and that

the combination of interferometric and radiometric radar data

constitutes a solid information source for this application.

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21ANSP_AC22 A Hybrid Meta-Heuristic for a Bi-Objective Stochastic Optimization of

Urban Water Supply System

The restoration and remodelling of the urban water supply system are

traditional challenges for water companies due to either aged existing

water supply networks or lodging expansion. These challenges involve

the uncertainties induced by their lengthy-planned prospects and the

impossible exact prediction of forthcoming events. In this regard,

correlations exacerbate unpredictable data and parameters and probably

undermine taking effective decisions in this context. Therefore, the

remodel and restoration decision of water supply systems must be made

using approaches that can effectively deal with correlation uncertainties.

The present study develops a bi-objective stochastic optimization model

that can handle interrelated uncertain parameters in the water supply

system remodelling and restoration issue. The proposed mathematical

model is validated using the data of the Mashhad Plain water supply

system as a real case study, followed by performing and comparing

different levels of conservatism and reliability. As a complex

optimization problem, an efficient algorithm is needed to solve the

problem. To this end, a hybrid meta-heuristic algorithm, which is a

combination of the Red Deer Algorithm (as a newly introduced nature-

inspired heuristic) and Simulated Annealing (as a traditional local

search algorithm), is proposed. Considering the advantages of these

algorithms, it is possible to alleviate the disadvantages of current

methods when solving large-scale networks. Finally, an extensive

comparison and discussion are made and then the main findings with

practical solutions are presented to significantly evaluate the proposed

model and algorithm

21ANSP_AC23 High-Accuracy Adaptive Low-Cost Location Sensing Subsystems for

Autonomous Rover in Precision Agriculture

With the prosperity of artificial intelligence, more and more jobs will be

replaced by robots. The future of precision agriculture (PA) will rely on

autonomous robots to perform various agricultural operations. Real time

kinematic (RTK) assisted global positioning systems (GPS) are able to

provide very accurate localization information with a detection error

less than ±2 cm under ideal conditions. Autonomously driving a robotic

vehicle within a furrow requires relative localization of the vehicle with

respect to the furrow centerline. This relative location acquisition

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requires both the coordinates of the vehicle as well as all the stalks of

the crop rows on both sides of the furrow. This extensive number of

coordinate acquisitions of all the crop stalks demand onerous

geographical survey of entire fields in advance. Additionally, real-time

RTK-GPS localization of moving vehicles may suffer from satellite

occlusion. Hence, the above-mentioned ±2 cm accuracy is often

significantly compromised in practice. Against this background, we

propose sets of computer vision algorithms to coordinate with a low-

cost camera (50 US dollars), and a LiDAR sensor (1500 US dollars) to

detect the relative location of the vehicle in the furrow during early, and

late growth season respectively. Our solution package is superior than

most current computer vision algorithms used for PA, thanks to its

improved features, such as a machine-learning enabled dynamic crop

recognition threshold, which adaptively adjusts its value according to

the environmental changes like ambient light, and crop size. Our in-field

tests prove that our proposed algorithms approach the accuracy of an

ideal RTK-GPS on crosstrack detection, and exceed the ideal RTK-GPS

on heading detection. Moreover, our solution package neither relies on

satellite communication nor advance geographical surveys. Therefore,

our low-complexity, and low-cost solution package is a promising

localization strategy as it is able to provide the same level of accuracy

as an ideal RTK-GPS, yet more consistently, and more reliably, as it

requires no external conditions or hassle of the work demanded by RTK-

GPS. 21ANSP_AC24 HISTIF: A New Spatiotemporal Image Fusion Method for High-

Resolution Monitoring of Crops at the Subfield Level

Satellite-based time-series crop monitoring at the subfield level is

essential to the efficient implementation of precision crop management.

Existing spatiotemporal image fusion techniques can be helpful, but

they were often proposed to generate medium-resolution images. This

study proposed a high-resolution spatiotemporal image fusion method

(HISTIF) consisting of filtering for cross-scale spatial matching

(FCSM) and multiplicative modulation of temporal change (MMTC).

In FCSM, we considered both point spread function effect and geo-

registration errors between fine and coarse resolution images.

Subsequently, MMTC used pixel-based multiplicative factors to

estimate the temporal change between reference and prediction dates

without image classification. The performance of HISTIF was evaluated

using both simulated and real datasets with one from real Gaofen-1 (GF-

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1) and simulated Landsat-like/Sentinel-like images, and the other from

real GF-1 and real Landsat/Sentinel-2 data on two sites. HISTIF was

compared with the existing methods spatial and temporal adaptive

reflectance fusion model (STARFM), FSDAF, and Fit-FC. The results

demonstrated that HISTIF produced substantial reduction in the fusion

error from cross-scale spatial mismatch and accurate reconstruction in

spatial details within fields, regardless of simulated or real data. The

images predicted by STARFM exhibited pronounced blocky artifacts.

While the images predicted by HISTIF and Fit-FC both showed clear

within-field variability patterns, HISTIF was able to reduce the spectral

distortion more significantly than Fit-FC. Furthermore, HISTIF

exhibited the most stable performance across sensors. The findings

suggest that HISTIF could be beneficial for the frequent and detailed

monitoring of crop growth at the subfield level.

21ANSP_AC25 Plant Breeding Evaluation Based on Coupled Feature Representation

With the rapid development of improved breeding equipment and

information technology, computer-aided decision-making in plant

breeding evaluation can help solve the problems associated with high-

throughput demand and insufficient experience of breeders in modern

large-scale field breeding experiments. Many linear models have made

great contributions to the development of breeding evaluation although

they are based on a wrong assumption of attribute independence. This

paper proposes a unified coupled representation that integrates intra-

coupled and inter-coupled relationships to capture the interdependence

among quantitative traits by addressing coupling context and coupling

weights. Moreover, a hybrid scheme of the linear correlation and ordinal

relation is introduced to express the coupling relationship with a preset

parameter that balances the contributions so as to capture both relative

and absolute performance in cultivar selection and breeding evaluation.

A framework that includes data preprocessing, coupled data

representation, feature selection, prediction model construction, and

assisted decision-making is our overall solution for the plant breeding

evaluation task. Experiments on real plant breeding data sets

demonstrated the effectiveness of coupled representation for elucidating

the quantitative phenotypic traits and the advantages of the proposed

plant breeding evaluation algorithm compared with benchmark

algorithms.

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21ANSP_AC26 Measurement of Potato Volume With Laser Triangulation and Three-

Dimensional Reconstruction

Potato grading is related to weight. Three-dimensional (3D)

reconstruction can provide highly accurate volume measurements of

potatoes, which can help farmers to analyse their phenotypic

characteristics and grade them. Considering their low cost and the

required accuracy, a monocular camera and line laser were used to build

a potato phenotype determination scanning device. The system obtains

coordinates along the surface of a potato, collects laser light reflected

from the surface in real time, and completes the coordinate calculation

of the original points using the triangulation method. However, the

original point clouds lose large areas of point clouds at the top and

bottom of the potato. Point cloud repair is carried out by interpolation

of points. In addition, the surface point cloud is smoothed. Finally, the

generated point cloud is used for 3D reconstruction and volume

calculation. In a volume error analysis test, potatoes are divided into

calibration and verification groups. First, linear regression is used to

relate the real and measured potato volume, and then the density of the

potato is calculated. The volume and mass of potatoes in the verification

group are measured by the device, and the standard volume and mass

are measured manually. The results show that the average relative error

in measured volume is −0.08%, and the average relative error in

estimated mass is 0.48%. These results indicate that the combination of

a line laser and a single camera provides accurate measurements of

potato volume that can be used for yield estimation and potato grading.

21ANSP_AC27 Arduino based automated sericulture system

Sericulture is a science which deals with rearing of silk worms and

production of silk. In India, most of the rural livelihood is sericulture

and is the base for financial, social, political and intellectual

advancements and up liftment since. Silk is called the queen of textiles

due to its glittering luster, softness, elegance, durability, and tensile

properties. Although there are several commercial species of silkworms,

bombyx mori is the most widely used. Silkworm is one of the important

domesticated insect, which produces rich silk thread in the form of

cocoon by consuming mulberry leaves during larva period. But during

the transformation from Larva to Silk, the silk worm has to pass many

phases. In each phase the silkworm monitoring is a greatest challenge

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for the farmer. Therefore, in this paper we proposed a method for

automation in sericulture system using arduino board. It deals with the

regulating of climatic conditions such as temperature and humidity in

the farm

21ANSP_AC28

Automated Smart Sericulture Based on IoT and Image Processing

Technique

The silkworms for the silk production is the heart of sericulture. The

second largest country in the manufacturer of silk is India. Or India is

the country where the creator of silk is second place. The root of

sericulture in India is economic, social, political and cultural. In each

stages of the healthy swell silkworms the salient artefact is humidity and

temperature, particularly during the circumstance of larva. Considering

the rearing swell silkworms the interpretative contention are bactericide

or disinfection. In our project, we dispense an ARM7 to plot the realtime

because to observe or keep track of the silkworms. Image processing is

help to recognize the infection or ill health and non-identical stages of

the silkworms. Our specimen keep up the collection of the real time

statistics by using ARM7 Controller. The total organization or structure

is statistics and execute with help of ARM7 controller. In identical

stages of the silkworm the ARM7 is check or control the atmospheric

environment or surrounding inside the room of the silkworms rearing.

Here we are using the web camera to detect the ill health silkworms and

spray the respective medicines or pesticides.Minimize the manual

intervention of the farmer by automating the process of irrigation of

mulberry plantation and also testing the temperature and controlling the

silkworm rearing unit by using a ARM7 board. Image processing

technique mainly used to find out the colour change in the silkworms

body. It indicates the non identical stages such as black worms and

swallow worms indicates the diseases worms

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21ANSP_AC29 RiceTalk: Rice Blast Detection using Internet of Things and Artificial

Intelligence Technologies

Rice blast is one of the most serious plant diseases. Many rice blast

management approaches require know-how of experienced farmers or

agronomists. Monitoring the farm for disease detection is labour

intensive and time consuming. By using Internet of Things (IoT) and

artificial intelligence (AI), we are able to detect plant diseases more

efficiently. Existing AI and IoT studies detect plant diseases by images

or non-image hyper spectral data, which require manual operations to

obtain the photos or data for analysis. Also, image detection typically is

too late as rice blast may already spread to other plants. Based on an IoT

platform for soil cultivation, we develop the RiceTalk project that

utilizes non-image IoT devices to detect rice blast. Unlike the image-

based plant disease detection approaches, our agriculture sensors

generate non-image data that can be automatically trained and analysed

by the AI mechanism in real time. The beauty of RiceTalk is that the AI

model is treated as an IoT device and is managed like other IoT devices.

In this way, our approach significantly reduces the platform

management cost to provide real-time training and predictions. We also

propose an innovative spore germination mechanism as a new feature

extraction model for agriculture. In the current implementation, the

accuracy of the RiceTalk prediction on rice blast is 𝟖𝟗. 𝟒%.

21ANSP_AC30 The Smart Image Recognition Mechanism for Crop Harvesting System

in Intelligent Agriculture

This study proposed a harvesting system based on the Internet of Things

technology and smart image recognition. Farming decisions require

extensive experience; with the proposed system, crop maturity can be

determined through object detection by training neural network models,

and mature crops can then be harvested using robotic arms. Keras was

used to construct a multilayer perceptron machine learning model and

to predict multiaxial robotic arm movements and position. Following

the execution of object detection on images, the pixel coordinates of the

central point of the target crop in the image were used as neural network

input, whereas the robotic arms were regarded as the output side. A

Mobile Net version 2 convolutional neural network was then used as the

image feature extraction model, which was combined with a single shot

multibox detector model as the posterior layer to form an object

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detection model. The model then performed crop detection by collecting

and tagging images. Empirical evidence shows that the proposed model

training had a mean average precision (mAP) of 84%, which was higher

than that of other models; a mAP of 89% was observed from the arm

picking results.

21ANSP_AC31 Red-Edge Band Vegetation Indices for Leaf Area Index Estimation

From Sentinel-2/MSI Imagery

The estimation of leaf area index (LAI) from optical remotely sensed

data based on vegetation indices (VIs) is a quick and practical approach

to acquire LAI over vast areas. Reflectance in the red-edge bands is

sensitive to vegetation status, and its information is thought to be useful

in agricultural applications. Based on three red-edge band observations

(represented as RE1, RE2, and RE3 for bands 5–7) from the

Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this

article aims to investigate the feasibility and performance of using red-

edge bands for LAI estimates with the VI method and ground-measured

LAI data sets. Sensitivity analysis from PROSAIL simulations revealed

that RE1 is mainly affected by the influence of the leaf chlorophyll

content, and this uncertainty should not be ignored during LAI

estimation. For the normalized difference vegetation index (NDVI),

modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic

range vegetation index (WDRVI), the optimal combination of Sentinel-

2 bands for LAI estimation was RE2 and RE3, with a minimum root-

mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were

proposed to exploit the full content of the red-edge bands of Sentinel-2,

and their performance in LAI estimation improved slightly. However,

both 2-band red-edge VIs and 3-band red-edge VIs remained slightly

saturated at high LAI levels; therefore, a segmental estimation with a

threshold was suggested for large LAIs. The results indicate that the

optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are

effective tools for crop LAI estimation in multiple-growth stages with

Sentinel-2 MSI images.

ieee agriculture 2020-21 code title and abstract

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