ios press an ontology-based automation system: a case ... · system can offer better fertilization...

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An Ontology-based Automation System: ACase Study of Citrus FertilizationJianwei Liao a,* Yi Wanga Jinyuan Wanga and Xiao Wena

aCollege of Computer and Information Science, Southwest University of China, Beibei, Chongqing, China,

400715.

E-mails: [email protected], , ,

Editors: First Editor, University or Company name, Country; Second Editor, University or Company name, CountrySolicited reviews: First Solicited Reviewer, University or Company name, Country; Second Solicited Reviewer, University or Company name,CountryOpen reviews: First Open Reviewer, University or Company name, Country; Second Open Reviewer, University or Company name, Country

Abstract. This paper conducts a motivation case study about automatic fertilization for citrus planting, to illustrate the feasibilityand applicability of ontology-based automation systems in precision agriculture. Specifically, we first build a citrus fertilizationontology on the basis of the citrus production knowledge in the forms of texts, tables and pictures from technical reports andbooks. Next, we utilize semantic techniques, including RDF-based (Resource Description Framework) representation, semanticreasoning (The Ontology Web Language, OWL), and probability modeling, to manage the fertilization ontology, for providingintegrated and accurate fertilization recommendations. Then, we integrate the constructed ontology with an automatic fertiliza-tion machine, to create our target semantic-based automation system. At last, we run experiments with our proposed system,and compare its outputs with the reference values advised by the agri-professionals of citrus planting. The results show that oursystem can offer better fertilization recommendation services, to trigger automatic production.

Keywords: Semantics, Ontology, Automation System, Citrus Fertilization, Precision Agriculture

1. Introduction

Citrus is the main fruit type grown in tropical, andsub-tropical climate areas of more than 150 countriesin the world [1]. Specifically, the ecological environ-ment in Chongqing, China is suitable for the growth ofcitrus. According to the official statistics, the total cit-rus planting area in Chongqing was nearly 1.26⇥ 105

hm2, and the overall output was more than 1.8 ⇥ 106

tons in 2011, which resulted in 0.5 billion dollars in-comes [2].

However, a major part of citrus orchards in Chongqinghave been reclaimed in hill dominated geography,which generally has poor soil quality and complicatedsoil conditions [4]. Due to lacking of expert knowledge

*Corresponding author. E-mail: [email protected].

about the soil fertility and the growth status of citrus,conventional fertilizing methods based on the accumu-lated farming experience, have subsequently causedsome problems including excessive fertilization, defi-cient fertilization, and inappropriate fertilization [2].Without doubts, these issues severely hinder the im-provements of quantity and quality of citrus produc-tion.

On the other side, the rapid development of urban-ization and the outflow of rural labor force in the back-drop, have lead to scarcity of rural labor and higherlabor expenses in Chinese traditional agriculture [5].That is to say, when employing the conventional pro-duction and management routines, the cost of manag-ing a commodity orchard is greatly increased, so thatthe problem of lacking of labor and high cost of laborhas become one of the biggest obstacles to the devel-opment of China’s fruit industry [6, 7]. As a result, a

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2 J. Liao et al. / Instructions for the preparation of a camera-ready paper in LATEX

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number of automation systems have been introduced infruit industry, such as automatic irrigation/fertilizationmachines and automatic growing/harvest machines, tocut down the labor cost [8, 9].

Nevertheless, such machines have pre-defined rulesto perform corresponding activities, and the operatorsshould configure parameters after understanding whatkind of activities are expected. But, many growers areshort of expert knowledge to determine critical specifi-cations (such as types and quantities of fertilizers whenperforming fertilization), to manipulate the machinefor conducting proper activities. This paper presents anovel, ontology-based automatic fertilization system,which is able to intelligently direct accurate automa-tion activities for citrus planting. In brief, this papermakes two contributions:

– We have built a citrus fertilization ontology, onthe basis of the citrus production knowledge.Then, fertilization decisions offered by seman-tic processing and probability modeling withinthe ontology, can be fed to a machine for au-tomatically triggering appropriate citrus fertiliza-tion. Different from conventional methods, theproposed ontology-based fertilization can be in-telligently adjusted, according to the observed in-formation about the growth status of citrus andsoil-relevant conditions.

– We provide preliminary evaluation on the Zoor ir-rigation and fertilization machine, by equippingwith our citrus fertilization ontology. As our mea-surements indicate, the proposed system can pro-vide a more appropriate fertilization output, com-pared to the default, fixed fertilization applica-tion, while it remains highly transparent on thelevel of professional knowledge on citrus plant-ing.

sec

2. Background and Related Work

This section introduces related work focusing onautomation systems, specially on semantic-based au-tomation systems.

Automation Systems in Precision Agriculture. Tosave the labor cost in agricultural production, certainautomatic fertilization systems have been implemented[6, 8, 9]. However, these systems cannot yield adaptivefertilization according to the growth status of plants, aswell as other environment conditions. Furthermore, C.

Goumopoulos et al. [10] have proposed plant-drivencrop management system in order to perform precisionagriculture applications. After monitoring soil, cropand climate in a field by employing IoT sensors, thedecision-support module of the system can proactivelydeliver appropriate treatments, such as fertilization andpesticide application, for specific parts of a field in realtime.

Specially, for better promoting automatic produc-tion in citrus planting, C. Wang et al. [11] have pro-posed a Local Binary Patterns (LBP) feature-basedmethod to count immature green fruits, by equippingthis approach within an automatic machine.

Semantics and Ontology. The database systems areefficient in handling big data (i.e. structured items)nowadays. However, an apparent shortcoming of thistechnique is because of its “closed world" assumption,it is difficult to merge and manage incomplete informa-tion [12]. In addition, while using text retrieval basedtechniques for desired information, the well-knownproblems of noise and bad recall have to be taken intoconsideration [13]. Obviously, it is not easy to figureout the best match while some strategies are used torefine or composite the results, as the database systemdoes not present any explanation or inferred informa-tion on them.

In order to overcome such limitations, Z. Laliwalaet al. [14] applied a semantic and rule based event-driven SOA (Service Oriented Architecture) informa-tion system, to demonstrate how the semantic technol-ogy can be employed for expressing common vocab-ulary, knowledge and automation. They also demon-strated how rules could be leveraged to offer behav-ioral knowledge, constraints and reaction to agricul-tural events. To sum up, an ontology intends to cap-ture domain knowledge in a generic way and providesa commonly agreed understanding of a domain [16].Thus, it can be treated as a model of representing or-ganized knowledge in a given domain [3].

Some researchers combine the agricultural systemsand ontologies to facilitate the seamless and mean-ingful information integration and knowledge interfer-ence. Specially, Y. Wang et al [4] have produced threecitrus decision services including fertilization, nutrientimbalance, and irrigation/drainage on the basis of se-mantic knowledge, for serving citrus planting. Thus,the farmers can retrieve the expected services by ac-cessing the website (it offers the citrus decision ser-vices), to direct farming activities. But their releasedontology has very limited knowledge about types and

J. Liao et al. / Instructions for the preparation of a camera-ready paper in LATEX 3

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IoT Sensors�

Semantic Reasoning ( fertilization decision making: analysis & state assessment)�

Actuation (context/decision delivery &

reaction: inter-communication)�

Machine’s Actuator�

Citrus Planting

Users (farmer)�

Observation�

Domain Knowledge (Fertilization Ontology)

Measurements (collecting sensor data

& analyzing them )�

Manually Input Data (growth status & soil condition)�

Source Data Translation (high-level context

interpretation & aggregation)�

Fig. 1. Workflow in the context of ontology-based automatic fertil-ization (Note:the route of collecting observation data by using sen-

sors is under construction).

quantities of fertilizers, and does not support semanticreasoning for learning new knowledge.

Semantic-based Automation Systems. Semantic tech-nologies are perfectly suitable for domain model-ing, classification, semantic search and deriving newinsights by semantic reasoning [17]. Consequently,adopting semantic knowledge to support precise auto-matic control is a trend in automation systems.

To improve the accuracy of the predicted energyconsumption in buildings, B. Yuce and Y. Rezgui [18]have proposed a semantic rule generation process us-ing a genetic algorithm and an artificial neural net-work. In [19], the Semantic Web technologies (basedon their newly constructed ontology) have been in-tegrated into building automation systems with otherWeb service compositions in the background, to dy-namically coordinate devices/services in accordancewith the context. As a consequence, the semantic-based automation systems have better effects in thetarget areas, compared with the traditional automationsystems.

G. Gharbi et al. [20] have proposed an autonomicarchitecture based on decision models, which are builton the top of ontologies, to benefit self-configuring andself-adapting information networks. Moreover, theyhave constructed an ecosystem-wide ontology, andthen deployed it in the testbed network. The relevantexperiments have efficaciously verified the effective-ness of their proposed architecture.

Although semantic techniques can benefit the au-tomation systems to yield attractive performance insome application contexts, there are not many automa-tion systems using semantic mechanisms, to performprecise agricultural production. This situation is be-cause of the difficulties in constructing a specific agri-cultural ontology, which must cover all required expert

knowledge to guide automatic production. Besides, itis a challenging task to seamlessly integrate the ontol-ogy framework with automation machines.

3. System Design and Ontology

As discussed, applying the semantic technology, i.e.ontology into agricultural information systems [4, 21],and industry automation systems [9, 18, 19] to achievebetter effectiveness is not new. But, to our best knowl-edge, leveraging the technique of ontology to transpar-ently, generate serviceable decisions for actuating au-tomation machines, to perform corresponding irriga-tion/fertilization activities in citrus cultivation, has notbeen found in the published literature. This section dis-cusses the specifications about the proposed ontology-based automatic fertilization system in citrus planting.

3.1. Functional Overview

Figure 1 illustrates a high level functional overviewof the proposed ontology-based automatic fertilizationsystem. In fact, our final goal of implementation in-tends to equip with IoT (Internet of Things) sensors,which are supposed to read plant/environmental con-ditions, for starting the cycle of automatic fertilization(as shown in the dotted rectangle in Figure 1). But, thedeployment of IoT sensors is under construction, sothat we currently input the plant/environmental condi-tions manually to emulate the information offered bythe IoT sensors. After that, the input information is in-terpreted and the high-level context information is thenderived. For example, the growth status of citrus, andthe type of soil are described as analogue signals thatmust be then converted to digital ones, since semanticprocessing in the following step requires digital inputdata.

Next, the reasoning system queries the semanticdatabase (i.e. ontology) with the input environmentalconditions, to infer a fertilization decision having typesof fertilizers and the relevant quantities. The com-ponents of semantic reasoning and probability-baseddecision system built with the fertilization ontology(“Domain Knowledge” in the figure) are the cores ofthe newly proposed automatic fertilization system. Thesystem organizes citrus fertilization knowledge in theform of ontology, which is “a formal, explicit specifi-cation of a shared conceptualization [23]", and storesall ontology data in a semantic database.


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Citrus Fertilizer acidBase!fertilizerType!fertilizationMethod!nitrogenRatio!phosphateRatio!potassiumRatio!farmerInstruction!!

NutrientContent

ChemicalContent

contain

contain

AcidFertilizer AlkalineFertilizer UnsutiableFertilizerOd1

FertilizerOd1

owl:disjoinWith

owl:unionOf

FertilizerOd1B1

FertilizerOd1B2

owl:equivalentClass

InOrganicFertilizer

OrganicFertilizer

Compost Fecaluria

Palntash

FarmyardManure

GreenManure

IronFertilizer

SulfurFertilizer

CalciumFertilizer

CalciumFertilizer

PhophaticFertilizer

NitrogenFertilizer

PotassicFertilizer

OilCake

Fig. 2. Overall structure of citrus fertilization ontology. Note: rounded rectangles indicate OWL classes, and rectangles mean the ontology

classes.

By referring the fertilization ontology, the systemsupports the functionality of semantic reasoning andprobability modeling. So that it can offer a guaran-tee that the quality of fertilization inference will in-crease monotonically as expected accuracy, with an ac-ceptable computational overhead. The details are sep-arately described in the following sections.

Eventually, the automation fertilization machine issupposed to be triggered to carry out correspondingactivities, when it receives fertilizing commands pro-vided by semantic computations with the newly con-structed ontology.

3.2. Fertilization Ontology

3.2.1. Ontology Construction

Apart from irrigation and disease diagnosis func-tionality, we specifically build our ontology to havefertilization knowledge on fertilizers, orchards and cit-rus growth conditions. Furthermore, we design certainOWL definitions for sustaining semantic reasoning, aswell as use the Naive Bayes classifier to enable proba-bility modeling.

In other words, we first collect fertilization knowl-edge on citrus planting from all kinds of sources in-cluding technical books, reports, and expert experi-ence. Then, we refine such knowledge into a cit-rus Fertilization Knowledge Database (the fertilizationknowledge is organized as pairs of question and an-swer), for providing basic data to build the ontology.

By taking advantage of the extracted fertilizationknowledge, it is not difficult to employ an ontology ed-itor, (TopBraid Composer is currently leveraged by us)to edit RDF triples, as well as relevant classes and at-

tributes. For example, a triple of [Fertilization at the

germination stage, Fertilization Amount, 10%-15%]

can be produced, according to a piece of refined fer-tilization knowledge. We have included more knowl-edge on frequently used fertilizers, soil properties, andcitrus growth conditions in the built ontology for bet-ter yielding fertilization recommendations. Moreover,our ontology contains many OWL definitions includ-ing OWL restrictions and set operations, to support se-mantic reasoning and probability modeling for the pur-pose of offering better fertilization decisions.

Note that producing RDF triples differs from creat-ing tables in a rational database, non-professional tech-nicians can easily append new triples to the ontology.Finally, we have completed the work on building thecitrus fertilization ontology, which then has requiredinformation to guide citrus fertilization.

3.2.2. Topology Structure of Ontology

The newly constructed ontology consists of a largequantity of expert knowledge about citrus fertilization,it therefore can benefit to diagnose fertilization prob-lems, for inferring proper fertilization recommenda-tions. That is to say, orchard growers just need to col-lect the information that can be easily observed, suchas the growth stage of citrus and other environmen-tal conditions, semantic processing can help them toyield a good fertilization decision. Figure 2 specificallyshows the hierarchy information about our fertilizationontology for citrus planting.

Moreover, Figure 3 illustrates a picture about somecritical RDF triples related to orchards and fertilizers,in our constructed citrus fertilization ontology. For ex-plicitly distinguishing one instance from another, dif-ferent resources are described with diverse colors. The


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Inorganic Fertilizer Sulfur

Fertilizer

Fertilizers suitable for Orchard 1

Fertilizers suitable for Orchard 3:

(part a)

Fecaluria

Chemicals of

Fertilizers

Green Manure


(part b)


Acid Fertilizer

Base Fertilizer :anon56

Phosphorus Pentoxide

Vicia Sepium Linn

Potassium Sulfate

:anon90

Foliar Spray

Slow Release

Cattle Urine

Diammonium Phosphate

:anon82

:anon81

Organic Fertilizer :anon95

Borax


(part b)

Human Excrement

:anon97

:anon96

:anon92 :anon93

:anon94

:anon31

Part of Fertilizers that

cannot be applied to Orchard 1:

alkaline fertilizers

Citrus Ferilizer

Compound Ferilizer


Potassium Dihydrogen

Posphate

Potassium Megnesium

Sulfate

Acid Soil

:anon89

:anon3

:anon6 :anon6

:anon6

:anon46

Phosphate Human Urine

Fertilizers that cannot

be applied to Orchard 4:

part a

Phosphatic Fertilizer


be applied to Orchard 3


(part a) Fertilizers suitable for Orchard 4

Ordinary Super

Phosphate



KEY:

:anon122 :anon32



:anon53

Type SubClassOf Proper Efficiency

Contain EquivalentClass FertilizationMethodUnionOf FertilizerType

Fig. 3. (Orchard and Fertilizer related) RDF triple topographies in the constructed citrus fertilization ontology, which are shown in Gruff [22].Note: each colored block represents a class or an instance of class, and the blocks having same color means they have the same ancestor class.

Each line indicates a property, that shows a relationship between two classes or instances.

newly constructed ontology consists of a large quantityof expert knowledge about citrus fertilization, it there-fore can benefit to diagnose fertilization problems, forinferring proper fertilization recommendations. That isto say, orchard growers just need to collect the infor-mation that can be easily observed, such as the growthstage of citrus and other environmental conditions, se-mantic processing can help them to yield a good fertil-ization decision, for better cultivating the citrus.

To identify the type and quantity of fertilizers whileconducting fertilization, our constructed ontology con-tains the detailed knowledge on the usage of fertiliz-ers. For now, the fertilizing nutrients of nitrogen (N),phosphate (P) and potassium (K) are taken into con-sideration, as these three kinds of nutrients are criticalto cultivating citrus. Figure 3 also shows the topogra-phies about the usage of these fertilizers, according todifferent contexts of citrus orchards. As seen, with re-spect to Od 1, relevant classes (blue blocks in the fig-ure) show specific information about suitable fertiliz-ers and unsuitable fertilizers.

By making use of the information represented withthese classes, we can yield a proper fertilization deci-

sion, including types and quantities of required fertil-izers, after certain semantic computations with the on-tology.

3.3. Semantic Reasoning for Proper Fertilizer Types

The newly constructed ontology contains the prop-erties about fertilizers, such as the fertilizer is organicor inorganic, acidic or alkaline, and the contents of ni-trogen, phosphorus and potassium. On the other hand,the orchards may have their own conditions, such asvaried soil conditions (acidic or alkaline, clay or sandy,and plant ages), and the citrus trees also have their owngrowth conditions. Therefore, it is rather difficult todefine fixed rules to directly guide citrus fertilizationfor different cases. Especially, the fixed rules cannotwork for the situations that have some undefined or-chard conditions.

We leverage the reasoning function of RDFS (RDFSchema) and OWL (The Ontology Web Language), todeduce what kind of proper fertilizers and their corre-sponding quantity ranges are commended, accordingto multifarious conditions of the orchards. The follow-ing two steps illustrate how we construct the ontology


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for the mentioned purpose, and how we achieve a fer-tilization recommendation by using semantic reason-ing:

Step 1: we build a set of the frequently used fertiliz-ers, and there are 37 types of fertilizers are included inour ontology at current stage, and organize them in theset of F. The relevant RDF triples (i.e. classes) shownin Figure 3 comprise the information about these kindsof fertilizers.

Step 2: we model the plants and soil conditions oforchards, for conducting accurate citrus fertilization.For each orchard, we design three classes, by using theinput information about the observed conditions of or-chards:

– Class A consists of applicable fertilizers for thecitrus plants in the orchards. As we know, morenitrogen is required in the case of small fruits.

– Class B has the information about what fertiliz-ers should not be applied to the orchards, on thebasis of their soil and other conditions. For ex-ample, alkaline-contained fertilizers are not rec-ommended to be applied for the orchard havingalkaline soil (typically, light yellow soil).

– Class C = A � B = A \ B, which is a set includ-ing the recommended fertilizers that can be uti-lized in the real usages, by considering both plantconditions and soil conditions. Since our selectedsemantic reasoner does not support complementreasoning, we have computed ClassC by usingSPARQL queries.

As a result, our application can take the fertilizers inthe set of (F � B), but the fertilizers in the set of C,i.e. (A � B) will be strongly recommended. Besides,the ranges of quantities of selected fertilizers will bedetermined on the basis of other conditions, such asthe yield of last year, location, the ages of plants, andthe contents of N, P, K in the fertilizers. Indeed, wemodel plants and soil conditions of orchards, and thenconstruct Class A and Class B in the ontology. Conse-quently, the proposed system can yield preferable fer-tilization decisions indicated by Class C.

3.4. Probability Modeling for Expected Fertilizer

Quantity

As discussed, semantic reasoning can deduce therequired fertilizer, and a suggested range of fertilizerquantity, however, varied symptoms of citrus may spe-cially affect the quantities of fertilizers. In other words,the fertilization system should identify excessive fer-

tilization or insufficient fertilization by analyzing theobserved symptoms of citrus, to achieve preferable fer-tilization decisions, according to the special cases. Asa result, the upper limit of quantity of fertilizer is pre-ferred in the case of fertilizer deficiency that deducedby the observed symptoms. Otherwise, the floor limitof quantity is expected for the case of fertilizer excessin citrus orchards.

We have taken advantage of a Naive Bayes classifier[24], to construct a probabilistic extension to our citrusontology, for estimating excessive or insufficient fer-tilization in citrus planting. To be specific, we have se-lected a number of independent features of citrus, suchas Leaf Blade and Fruit Peel, and each feature has itsown irrelevant values, refer Appendix A for more de-tails. Next, a feature instance of citrus, i.e. the valuesof n features (i.e. independent variables), can be de-picted as a vector X = (x1, . . . , xn). At last, we useEquation 1 to classify a given instance into a class ofexcessive fertilization or insufficient fertilization on aspecific fertilizer.

P(Y = ck | X = x) =P(Y = ck)

QjP(X( j) = x

( j) | Y = ck)Pk

P(Y = ck)Q

jP(X( j) = x( j) | Y = ck)

(1)

where k = 1, 2, . . . ,K, and it indicates that there areK classes of excessive or insufficient fertilization. Wecurrently have 3 types of fertilizers, and each of themhas 3 situations, which are Deficiency, Optimum, andExcess. Then, we have 9 classes, that are also namedas fertilization states.

In Equation 1, the label of P(Y = ck | X = x) repre-sents the probability of excessive fertilization or insuf-ficient fertilization regarding a specific fertilizer (i.e.ck), with the given features of X = x in citrus plant-ing. Because the value of

Pk

P(Y = ck)Q

jP(X( j) =

x( j) | Y = ck) is a constant, while the values of the fea-

ture variables are fixed, we can then simplify Equation1 as following:

y = argmaxck

P(Y = ck)Y

i j

P(X( j) = x( j) | Y = ck)

(2)

Appendix A also specifically presents the priorprobability distribution, the conditional probability,


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Table 1Specification of Citrus Orchard in our Evaluation (1 mu = 614.4 m

2).

Orchard Tree-age Last Year Yield Soil Properties Topography Sample Symptoms

Od 1 8 year 2000kg/muArgillaceous/Light-yellow(Alkaline, Zinic-deficiency)

Hill Peak Yellow Leaf & Low Fruit Setting

Od 2 8 year 1150kg/muArgillaceous/Red-yellow

(Acidic soil)Hill Side Normal

Od 3 7 year 1630kg/muSandy/Red

(Acidic, Zinic-deficiency)Hill Peak Few Flowers & Thick Peel

Od 4 5 year 1470kg/muSandy/Purple

(Neutral, Iron-deficiency)Hill Side Little Juice & Burned Leaf Margin

and the variable correlation with a consequence graphin Bayesian Networks of our ontology. For example,the features of Yellow Leaves and Low Fruit Setting

can be obliviously grouped to the class of Nitrogen De-

ficiency. After understanding the fertilization state ofcitrus trees, it is able to perform more precise fertiliza-tion by utilizing the preferred quantities of fertilizers.

4. An Example of Fertilization based on SemanticReasoning and Probability Modeling

This section shows how our ontology-based systemdoes yield attractive fertilization decisions about typesand quantities of fertilizers, on the basis of semanticreasoning and probability modeling.

4.1. Specifications of Targeted Orchards

In our preliminary evaluation, four real world or-chards of citrus numbered from 1 to 4, are used for ourexperiments, and all of these orchards may have variedgrowth conditions. Table 1 reports specifications aboutthe orchards. As a matter of fact, we dynamically cre-ate corresponding OWL classes for these orchards inthe ontology, when the operator input the observed in-formation, for intentionally triggering fertilization.

To guide better fertilization, we explore the symp-toms of excessive and deficient usages of fertilizingnutrients of N, P and K, and such information has beenalso included in our ontology. Then, it can adjust quan-tities of fertilizers, on the basis of varied symptoms.For instance, the system can deduce the symptom of“Yellow Leaf &Low Fruit Setting in Od 1” is probablycaused by nitrogen deficiency, so that the upper limitof recommended quantity of nitrogen is supplied.

4.2. Use of Semantic Reasoning and Probability

Modeling

By referring back to the design and implementationsection, we present a working example about apply-ing the nitrogen nutrient for Od 1 at the germinationstage, by employing semantic reasoning and probabil-ity modeling.

The suggested usage of pure nitrogen fertilizer forthe case in our example is 3.02kg/mu, accordingto the explicit fertilization knowledge in the ontology.But, not all fertilizers are suitable for a specific or-chard, such as acidic fertilizers are not recommendedfor zinc-deficient soil. Thus, we adopt the functionalityof semantic reasoning in our system to choose appro-priate types of fertilizers by considering all aspects offactors. Furthermore, the Naive Bayes classifier is uti-lized to determine preferable quantities of the selectedfertilizers.

To this end, our system creates two OWL classesabout suitable fertilizers and unsuitable fertilizers, i.e.Class A and Class B, introduced in the semantic rea-soning section, on the basis of soil conditions of Od

1. To be specific, it first creates two OWL sub-classesfor indicating alkaline fertilizers and chloride fertiliz-ers should not be used, according to the soil conditionsof Od 1. Then, our system makes an union class aboutunsuitable fertilizers (i.e. Class B), by simply mergingtwo sub-classes of unsuitable fertilizers. After that, agiven SPARQL query can be utilized to obtain the ra-tios of nitrogen content in all recommended nitrogenfertilizers. Refer Appendix B for more information onthe OWL class definitions and the relevant SPARQLquery. Then, the quantities of these fertilizers can becalculated by dividing the quantity of required pure ni-trogen by the ratios of nitrogen content in varied fertil-izers.


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More important, a little bit more Nitrogen fertilizerthan the average is expected for Od 1, because theNaive Bayes classifier in the ontology predicates thisorchard is probably lack of Nitrogen, by analyzing thesymptoms of Yellow Leaf & Low Fruit Setting. As re-ported in Table 2, our proposal consequently advisesemploying an organic fertilizer, i.e. 100kg/mu cattleurine for applying a little more nitrogen nutrient for Od

1 at the stages of germination/stabling fruits. On theother side, only the average quantity of pure nitrogenis recommended when adopting the most related work[4].

Similarly, the symptoms of Few Flowers & Thick

Peel in Od3 may caused by excessive nitrogen, and thecase of potassium excess may result in the symptomsof Little Juice & Burned Leaf Margin in Od4. Then,the system can carry out better fertilization recommen-dations on fertilizer quantities for the orchards of Od3

and Od4, from case to case.

4.3. Fertilization Recommendation Comparison

To roundly rate the effectiveness of semantic rea-soning, we have compared our mechanism with themost related work that does not support semantic rea-soning [4], to guide fertilization aiming at the selectedorchards. For fairness of comparison, we disabled thefunctionality of semantic reasoning in our system, toemulate the most related work, so that both mecha-nisms have the same explicit fertilization knowledge.

We have disclosed the fertilization usages of pure N,P, and K fertilizers at four growth stages of citrus plant-ing, i.e. the stages of Germination, Stabilizing Fruits,Swelling Fruits, and Picking Fruits. Table 2 presentsresults of sampled fertilization suggestions for the se-lected orchards, when using the most related work ofNon-reasoning and our system of Reasoning havingsemantic reasoning and probability modeling. Sinceexpert knowledge suggests to perform same fertiliza-tion activities for citrus at stages of Germination andStabilizing Fruits, both selected mechanisms yield thesame fertilization results at the mentioned two stages.

As seen in Table 2, Non-reasoning could benefitto producing relevant fertilization recommendationsabout the quantities of pure nitrogen, phosphorus andpotassium, on the basis of conditions of orchards. Al-though Non-reasoning does not enable semantic rea-soning and probability modeling, it can achieve a fer-tilization decision by using the explicit knowledge or-ganized as RDF triples.

On the other hand, our newly proposed mechanismlabeled as Reasoning, has overall considerations andweighs comprehensively so as to ensure preferablefertilization recommendations by using more properfertilizers with relevant quantities. Note that we onlyshow an example of fertilization recommendations, butthe applied fertilizers in real usages depend on avail-able quantities of fertilizers, and other prerequisites.

5. Deployment and Case Study

This section first describes deployment of our newlyconstructed ontology in a real-world automatic fertil-ization machine, for evaluating the proposed mecha-nism. It then presents experimental results and relevantdiscussions. At last, we will summarize the key pointsof our proposed approach.

5.1. Prototype of Fertilization Machine

To verify the feasibility of the proposed system,we have applied the semantic processing functional-ity into an automatic fertilization machine, made bythe Wuhan Zoor Water Saving Irrigation Company[25]. This small-scale machine aims to work in greenhouses, and triggers fertilization when it receives a rel-evant command about types and exact quantities of fer-tilizers. We have used a computer, which has the deci-sion support application, the constructed ontology andits running environment, as the external control termi-nal of the machine.

Since the automatic fertilization machine has merelythree cans for pre-loading three types of fertilizers, weonly perform experiments about using pure nitrogen,phosphorus and potassium fertilizing nutrients in ourcase study. To some extent, our proposal has the sameeffectiveness as the most related work (i.e. ontologywithout semantic reasoning) does in this case study, asit is not possible to select preferable fertilizers.

The operator can manipulate the computer to actuatethe fertilization. Specifically, the operator just needsto specify the information that can be easily observed,such as the conditions of soil, the location of orchard,and the growth status of citrus. Then, the proposed sys-tem will deduce a precise fertilization recommenda-tion, by querying the semantic database (i.e. ontology).Eventually, the machine triggers the electric valve tobegin a required activity of fertilization. Similarly, theelectric valve should be switched off, when the quan-tity of fertilization reaches the threshold.


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Table 2(Sampled) Fertilization Recommendations through Ontology with Semantic Reasoning or not [4] and(kg/mu)

Orchard Germination/Stabling Stage Swelling Stage Picking StageNon-reasoning Reasoning Non-reasoning Reasoning Non-reasoning Reasoning

Od 1

Pure N.(3.02)Pure P.(2.40)Pure K.(3.13)

Cattle urine(⇡100)

Pig manure(⇡300)

Rape cake(⇡20)

Pure N.(15.61)Pure P.

(12.48)Pure K.

(15.61)

Human urine(⇡200)

Rape cake(⇡150)

Yard manure(⇡2000)


Cattle urine(⇡80)

Pig manure(⇡240)

Rape cake(⇡16)

Od 2


FCMP(⇡3)

Lime N.(⇡5)PMS

(⇡4.5)


FCMP(⇡18)

Lime N.(⇡27)PMS

(⇡21.5)


FCMP(⇡2.8)Lime N.(⇡3.7)

PMS(⇡3.3)

Od 3


Plantash(⇡10)

Rape cake(⇡50)

Yard Manure(⇡50)ZSMH(⇡1.2)

Pure N.(11.04)Pure P.(8.33)Pure K.

(10.71)

Plantash(⇡52)

Rape cake(⇡260)

Yard Manure(⇡260)


Plantash(⇡8)

Rape cake(⇡40)

Yard Manure(⇡40)

Od 4


Lime N.(⇡6.8)

PMS(⇡6.5)ZSMH(⇡1.0)


Lime N.(⇡35)PMS

(⇡34)


Lime N.(⇡5.5)

PMS(⇡5.2)

FCMP: Calcium Magnesium Phosphate; PMS: Potassium Magnesium Sulfate; ZSMH: Zinc Sulfate Monohydrate.

5.2. Deployment Setup

We have deployed the ontology and then config-ured its run-time environment in a computer that hasa dual-processor Intel(R) E5800 3.20G Xeon-basedCPU with 4 GB RAM. And this computer has beenemployed as an external control panel of the automaticfertilization machine. We use AllegroGraph (AG) 6.1

as our semantic database for managing the ontology[26]. The Java decision service application gathers op-erator’s input data, and then employs Java Sesame API

for accessing AllegroGraph, to seek for a fertilizationdecision.

We compare our proposed mechanism with theexperience-based method in this case study. The experience-based method indicates the operator activates fertil-

ization, on the basis of their accumulated knowledge,and the farmers generally adopt this method to culti-vate citrus. In our evaluation, two citrus growers areinterviewed to collect the fertilization specificationsaccording to the different situations, and the averagedosage of fertilizers is reported in this section.

The four citrus orchards shown in Table 1 are alsoused in this real-world case study. Then, in our evalu-ation experiments, the operator is required to input theinformation about the age of citrus trees, the yield inlast year, the type of soil (e.g. clay and sandy), the ter-rain of the orchard (e.g. hilltop and hillside), and thegrowth status of citrus trees. After that, our proposedsystem analyzes the input data, and queries the seman-


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(a) Fertilization Recommendation Comparison for Od 1 (b) Fertilization Recommendation Comparison for Od �

(c) Fertilization Recommendation Comparison for Od � (�) Fertilization Recommendation Comparison for Od �

120% 140% 160% 180%

Germination Stablizing Swelling Picking

Reco

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Growth Stage of Citrus

N(Experience)N(Ontology)P(Experience)P(Ontology)K(Experience)K(Ontology)

0% 20% 40% 60% 80%

100% 120% 140% 160% 180%


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Ratio



0% 20% 40% 60% 80%

100%

120% 140% 160% 180%


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0% 20% 40% 60% 80%

100%

120% 140% 160% 180%


Reco

mm

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Expe

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Ratio



0% 20% 40% 60% 80%

100%

Fig. 4. Fertilization results at different stages of citrus planting

tic database, to form a fertilization decision for actuat-ing proper fertilization automatically.

5.3. Results

In general, the growers tend to prescribe invariablefertilizer rates as a safety management strategy in agri-cultural production [15]. But, the balanced applicationof nitrogen, phosphate, and potassium benefits main-taining attractive effects on citrus yield. In other words,the proposed ontology-based fertilization system aimsto better help the growers for cultivating citrus withprecise auto-fertilization, in accordance with variedcases.

5.3.1. Fertilization Accuracy

This section presents the results about fertilizationspecifications by employing the selected methods. Fig-ure 4 illustrates experimental results, and each sub-figure reports the fertilization suggestions targeting ata specific orchard.

In every sub-figure, the horizontal axis shows growthstages in citrus plating, and the vertical axis representsthe Recommendation/Expectation ratio. To put it from

another angle, the Recommendation metric means theadvised quantities of pure fertilizers suggested by oursystem or the interviewed citrus growers. The Expec-

tation metric signifies the required quantities of purefertilizers offered by a citrus agri-professional. It wasassumed that the information provided by the expertswas sufficient to guide the farmers to conduct cor-rect fertilization. Clearly, it is preferred to adopt thefertilization recommendation when the Recommenda-

tion/Expectation ratio is close to 100%. So that weleverage this ratio to estimate the gain/loss in fertiliza-tion accuracy resulted by our proposed system.

Regarding the labels in the legend of figure, the firstcapital letter means the type of fertilizer, and the wordgiven in parentheses represents the used recommen-dation schemes (i.e. Experience-based, or Ontology-

based). For instance, the label of N(Experience) signi-fies the case of the pure nitrogen usage provided by theexperience-based scheme.

As shown in the figure, the widely adopted experience-based scheme causes inadequate fertilization for theorchards having attractive yield in the last year. Italso sometimes causes excessive fertilization (e.g. fewcases in Od 2 and Od 3). The most attractive finding is


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Fig. 5. Time overhead required for generating fertilization recom-mendations at different stages of orchards.

about the Recommendation/Expectation ratio achievedby our newly proposed method is 100% in all cases.Namely, it could yield the consistent fertilization rec-ommendation as the expert does. We say that by us-ing our system, the citrus growers do not require hav-ing specific expert knowledge on fertilization in citrusplanting, they can also perform appropriate fertiliza-tion activities.

5.3.2. Overhead and Ontology Profile

We have also measured time overhead resulted bysemantic computations, i.e. semantic processing at dif-ferent growth stages in the selected orchards. Figure5 shows the time required for generating a proper fer-tilization recommendation, when we run evaluationexperiments with the implemented automatic system.Clearly, the time used to complete semantic compu-tations is less than 200ms in all cases, which is verybeneficial to trigger auto-fertilization in real time. Wecan also see that the time needed for semantic process-ing slightly varies from each other, that is because theinvolved information might be disparate in differentcases, which requires variable time to complete rele-vant semantic computations.

Table 3 reports the profile of our constructed fer-tilization ontology. As shown, it has 59 classes, andeach class is simply a name and collection of proper-ties that describe a set of target individuals. For exam-ple, the class of “swu:Fruit” has all properties aboutcitrus fruits in our constructed ontology. Also, there aremore than 3000 RDF triples, and the overall size ofdatabase is 519.41 MB, which includes the ontologyand its running environment.

Overall, the time overhead caused by performingsemantic computations and the space overhead intro-

Table 3Profile of the constructed citrus fertilization ontology

Property Item ValueCurrent Version ver 2.9

Number of Classes 59

Number of Triples 3056

Size of Core Ontology 279 KB

Size of Database 519.41 MB

duced by storing the constructed ontology (includingits runtime environment) is acceptable in practice, eventhough we are using embedded devices. In this paper,we do not report energy overhead caused by the ma-chine, because it is decided by machine’s specifica-tions, and not related to the semantic computations1.In brief, our proposed ontology-based automation fer-tilization system for citrus planting is feasible and ap-plicable in the real cases, as it does not cause too muchoverhead.

5.4. Summary

We emphasize two key observations from our eval-uation experiments. First, farmers can simply describethe growth status and environmental conditions in cit-rus planting, to conduct appropriate auto-fertilizationactivities, though they may lack of related expertknowledge. Second, by leveraging semantic process-ing, the proposed automatic fertilization system canyield better price/performance ratio according to theexperts’ expectations, when comparing with the con-ventional systems. We conclude that our newly pro-posed ontology-based automatic fertilization systemis able to significantly benefit the farmers to cultivatecitrus with the lowest labor expenses.

Furthermore, our implemented system has validatedthe feasibility of applying semantic techniques intoagriculture, and it offers a general solution for cul-turing plants with automation systems in precisionagriculture. In other words, the technical researchersfirst construct relevant ontologies for target applicationcontexts, and then integrate the ontologies with rele-vant automation systems, to support accurate automa-tion production in agriculture. Therefore, the agricul-tural producers can conduct proper farming activitiesby using such automation systems, to largely save la-bor cost and increase production.

1 In our case, the pump of the machine consumes 2.5kW per hour,according to the descriptions in the manual.


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6. Concluding Remarks

This paper has proposed an ontology-based fertiliza-tion automation system for citrus planting. Firstly, wehave built a citrus fertilization ontology by using ex-pert knowledge on citrus fertilization. Then, a prefer-able fertilization decision is deduced using semanticprocessing and probability modeling, on the top of thefertilization ontology, by feeding the input informationabout the growth status and soil conditions. The auto-matic machine is actuated to perform an expected fer-tilizing activity when the decision has been received.

Through a realistic case study, we realize that thisnewly implemented ontology-based fertilization sys-tem can offer all-round and precise information to per-form automatic fertilization in citrus planting. That isto say, the semantic knowledge expression and the in-telligent decision approach can be regulated and thencontribute to enhancing the precision rates for trig-gering automatic fertilization. Moreover, the ontology-based systems have an attractive feature of well flex-ibility, in contrast to conventional sophisticated rule-based systems.

We are now deploying the IoT sensors in citrus or-chards, and developing relevant programs to identifythe collected information including the soil type andsoil nutrients. As a result, the identified data are lever-aged as the input data to trigger proper fertilization ac-tivities in the near future.

Acknowledgements

This work was partially supported by “National Nat-ural Science Foundation of China (No. 61872299)",“Natural Science Foundation Project of CQ CSTC(No. CSTC2018jcyjA0964)", and “Hunan ProvincialNatural Science Foundation of China (No. 2018JJ2309)”.The authors would like to thank Dr. Sheng-Chuan Wuof Franz Inc. in Oakland, U.S.A, for his expert knowl-edge on ontology.

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Appendix A

Prior probability distribution and conditional probability distribution in the Naïve Bayes classifier of our fertilization ontology

For the purpose of identifying the situations of fertilizer deficiency or excess in citrus orchards by analysing the symptoms of citrus, we have incorporated the Naïve Bayes classifier into the newly constructed fertilization ontology. As a consequence, it is able to yield preferable fertilization decisions on fertilizer quantities. For example, the upper limit of quantity of nitrogen is expected in the case of the citrus trees are lack of nitrogen.

To this end, we have selected independent 14 features of citrus trees, and defined relevant values of these features, which are shown in Table 1.

Table 1. Considered features and their values on fertilization in citrus planting

Features of citrus Values

①Colour of Leaves �� ②Fallen Leaves? �� ③Burned Leaf Margin? �� ④Thickness of Peel �� ⑤Size of Fruit �� ⑥Level of Juice Content ��

⑦Fruit Colouring Time ��

⑧Fruit Acidity ��

⑨Level of Fruit Setting ��

⑩Smoothness of Fruit Peel � ��

⑪Fruit shrinkage? ��

⑫Shoot Shrivelling? ��

⑬Thickness of Shoot ��

⑭Quantity of Flowers �

After analysing the statistical data on citrus fertilization, we have obtained the prior probability distribution, with respect to different fertilization state on the fertilizers of Nitrogen, Phosphate, and Potassium, as demonstrated in Table 2.

Table 2. Prior probability distribution of fertilization in citrus planting

Fertilization (N, P, K) state Prior probability

Nitrogen Deficiency �� Nitrogen Optimum �� Nitrogen Excess �� Phosphate Deficiency �� Phosphate Optimum �� Phosphate Excess ��

Potassium Deficiency ��

Potassium Optimum ��

Potassium Excess ��

Furthermore, we have computed the conditional probability distribution, by considering the observed features (shown in Table 1) and our fertilization state (reported in Table 2). Table 3 demonstrates the results.

Table 3. Conditional probability distribution of fertilization in citrus planting (%)

Features & values

Nitrogen Defi. Opt. Ex.

Phosphate Defi. Opt. Ex.

Potassium Defi. Opt. Ex.

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�� Note: Defi. indicates deficiency; Opt. means optimum; Ex. implies excess. The label of ①-1 represents the condition is the first feature having the first value, which are explicitly shown in Table 1.

By using the Naïve Bayes classifier supported in our ontology, we have yielded the variable correlation with a consequence graph in Bayesian Networks, as seen in Figure 1. We summarize that both insufficient fertilization and excessive fertilization may cause a number of symptoms in citrus planting. In other words, the probability extension of our ontology is able to deduce the fertilization state by analysing the observed symptoms.

Fig. 1. The consequence graph of citrus fertilization (the cases of optimum fertilization are ignored). Big circles are the states of excessive or insufficient fertilization, and small circles are the symptoms of citrus that have been reported in Table 1.

----------------------------------------------------------------------------------------------------------------------- [This part should not be shown with the main manuscript if the paper gets accepted, listing this calculation example is only for illustrating how we classify a given feature instance of citrus into one of 9 fertilization states]

To demonstrate the process of identifying the target class for a given instance of collected values of features in citrus trees when using the Naïve Bayes classifier, we have list the details about the citrus trees in Od 1 is lack of the nitrogen nutrient.

The symptoms in Od 1: Yellow Leaf and Low Fruit Setting, and we represent the symptoms as the vector of X.

We then calculate all values by using the following equation (the numerator part of Equation 2 in the paper), when k=1, 2, …9, as we have taken 9 fertilization states into account.

1. ND | X = 0.682 * 0.923 * 0.923 = 0.581015578 (ND: Nitrogen Deficiency, X: feature vector)

2. NO | X = 0.251 * 0.032 * 0.024 = 0.000192768 (NO: Nitrogen Optimum)

3. NE | X = 0.067 * 0.141 * 0.654 = 0.006178338 (NE: Nitrogen Excess)

4. PD | X = 0.411 * 0.002 * 0.368 = 0.000302496 (PO: Phosphate Deficiency)

5. PO | X = 0.543 * 0.044 * 0.012 = 0.000286704 (PO: Phosphate Optimum)

6. PE | X = 0.046 * 0.014 * 0.025 = 1.288e-05 (PO: Phosphate Excess)

7. KD | X = 0.285 * 0.712 * 0.347 = 0.07041324 (KO: Potassium Deficiency)

8. KO | X = 0.451 * 0.013 * 0.034 = 0.000199342 (KO: Potassium Optimum)

9. KE | X = 0.264 * 0.103 * 0.282 = 0.007668144 (KO: Potassium Excess)

According to Equation 2 shown in the paper, the largest one from the above 9 values corresponding to 9 fertilization states, i.e. the first item is our target to be selected. In brief, we can deduce that the features of Yellow Leaf and Low Fruit Setting in Od 1 means the state of Nitrogen Deficiency. As a consequence, the fertilization system can recommend using the upper limit of quantity of Nitrogen fertilizer. # Note that all computed values do not mean the probability, and the sum of these values is not 1.0.

Nitrogen excess

Potassium excess

⑫-1①-1⑤-2 ⑨-1 ⑭-1⑬-1 ⑩-2 ⑤-1 ④-1 ③-1⑦-2④-2 ⑦-1 ⑩-1⑧-2 ⑧-1 ⑥-2

Potassium deficiency

②-1⑥-1 ⑪-1

Phosphorus deficiency

①-3

Phosphorus excess

Nitrogen deficiency

Appendix B

An example of OWL class definitions and SPARQL for Od 1

We present a working example about applying the nitrogen nutrient for Od 1 at the germination stage, by employing semantic reasoning of our constructed ontology.

In order to yield a fertilization recommendation for Od 1, our system creates two OWL classes about suitable fertilizers and unsuitable fertilizers, i.e. Class A and Class B introduced in Section III-C, on the basis of soil conditions of Od 1.

To be specific, it first creates two OWL sub-classes for indicating alkaline fertilizers and chloride fertilizers should not be used, according to the soil conditions of Od 1. And the source codes of two corresponding OWL definitions are shown in Figures 1(a) and 2(b). Then, our system makes an union class about unsuitable fertilizers (i.e. Class B), as described in Figures 1(c), by simply merging two sub-classes reported in Figures 1(a) and 1(b). Moreover, it is suggested to employ organic fertilizers because of the alkaline soil of Od 1, and Figure 1(d) describes the source codes of this OWL class.

Fig. 1. Generated source codes of OWL class definitions about suitable and unsuitable fertilizers with respect to the orchard of Od 1.

After that, a given SPARQL query can be utilized to obtain the ratios of nitrogen content in all recommended nitrogen fertilizers, as illustrated in Figure 2.

Fig. 2. A SPARQL query for knowing the ratios of nitrogen content in the set of suitable fertilizers targeting at Od 1.

:Fer%lizerOd1B1��aowl:Class;��rdfs:label"unsuitabletoOd1:chloride"@en;rdfs:subClassOf:CitrusFer8lizer;�owl:equivalentClass[aowl:Restric8on;

�owl:hasValue:Chloride;�owl:onProperty:contain��].

:Fer%lizerOd1B2��aowl:Class;��rdfs:label"unsuitabletoOd1:alkaline"@en;rdfs:subClassOf:CitrusFer8lizer;owl:equivalentClass[aowl:Restric8on;

owl:hasValue:Alkaline;�owl:onProperty:acidBase��].

(a) Part unsuitable fertilizers for Od1 (I. chloric) � (b) Part unsuitable fertilizers II for Od1 (II. alkaline) �

:Fer%lizerOd1aowl:Class;rdfs:label"suitablefer8lizersforOd1"@en;rdfs:subClassOf:CitrusFer8lizer;owl:equivalentClass[aowl:Restric8on; ��owl:hasValue:OrganicFer8lizer;owl:onProperty:fer8lizerType].

(d) Suitable fertilizers for Od1 (i.e. Class A) �

:UnsuitableFer%lizerOd1aowl:Class;rdfs:label“unsuitabletoOd1:all"@en;rdfs:subClassOf:CitrusFer8lizer;owl:unionOf(:Fer8lizerOd1B1:Fer8lizerOd1B2).

(c) Unsuitable fertilizers for Od1(i.e. Class B)�

SELECT ?f ?Nratio WHERE {?f rdf:type :Fer$lizerOd1 . NOT EXISTS {?f rdf:type :UnsuitableFer$lizerOd1 .}

?f :contain ?b1 . ?b1 :contain :Nitrogen . ?b1 :ratio ?Nratio .}

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