DIAGNOSING PESTS AND DISEASES ON PINEAPPLE USING THE BAYES THEOREM Текст научной статьи по специальности «Медицинские технологии»

UDC 632; DOI 10.18551/rjoas.2023-02.15 DIAGNOSING PESTS AND DISEASES ON PINEAPPLE USING THE BAYES THEOREM

Wahyuni Meri Sri, Marbun Boy Putra Tama, Riansah Wahyu

STMIK Triguna Dharma, Indonesia *E-mail: meri.sriwahyuni@gmail.com

ABSTRACT

Pineapple plants grow in tropical climates and have long been cultivated. Pineapple plants can be harvested 18-24 months after planting. Pineapple contains vitamins A and C and calcium, phosphorus, magnesium, iron, sodium, potassium, dextrose, sucrose (cane sugar), and bromelain enzymes beneficial for the body. Pineapples grow using fibrous roots to absorb organic matter and water from the soil. However, like other plants, pineapple plants also face problems with pests and diseases, causing a decrease in fruit quality and even leading to crop failure and losses for farmers. One of the causes of pests and diseases is no replanting for years because farmers lack knowledge in cultivating pineapple plants. For this reason, applying the expert system employing the Bayes Theorem is necessary to find suitable solutions in dealing with pests and diseases in pineapple plants. The system is built using a web-based programming language so that farmers can access the system created anytime and anywhere.

KEY WORDS

Pineapple, pests and diseases, expert system, Bayes theorem.

Pineapple plants grow in tropical climates and have long been cultivated. Pineapple plants can be harvested 18-24 months after planting. Pineapple contains vitamins A and C and calcium, phosphorus, magnesium, iron, sodium, potassium, dextrose, sucrose (cane sugar), and bromelain enzymes beneficial for the body (Rodliyatun et al., 2019). Pineapple plants have fibrous roots. They grow in soil with many organic elements and can store water in the axils to survive dry conditions for a relatively long time. However, like other plants, pineapple also faces problems with pests and diseases. Pests and diseases are major problems for pineapple farmers because they interfere with the growth and development of pineapple plants; the pests and diseases vary, making them difficult to diagnose (Maharani et al., 2021). The pests and diseases cause a decrease in fruit quality and even lead to crop failure and losses for farmers. One of the causes of pests and diseases is no replanting for years and no crop rotation because farmers lack knowledge in cultivating pineapple plants and unsuitable nutrients for pineapple plants. For this reason, applying the web-based expert system is necessary to find suitable solutions in dealing with pests and diseases in pineapple plants. The expert system works just like an expert; it helps analyze the pests and diseases in plants (Setyaputri et al., 2018). One of the methods in the expert system is the Bayes Theorem Method—the method for overcoming data uncertainty by predicting future opportunities based on previous experience (Puspitasari et al., 2021). Previous studies have been using the Bayes Theorem, including diagnosing anemia (Studi Sistem Informasi & Triguna Dharma, 2017), detecting refractive eye disease (Rachman, 2020), diagnosing Oppo mobile phone damage (Arif et al., 2021), diagnosing irritable bowel syndrome (IBS) (Atmaja et al., 2022), and helping with motorcycle damage (Suzuki Satria f150) (Setiawan et al., 2020). Our study aims to help pineapple farmers accurately determine the types of pests and diseases on their plants using the developed web-based system employing the Bayes Theorem Method.

METHODS OF RESEARCH

The research process is the stage where researchers collect data and information needed and then analyze the data to answer the research questions. We employed the Research and Development design in this present study.

The data collection included observations (collecting data through direct observations at pineapple orchards and literature research (our reference was primarily books and local journals). The expert system adopts human knowledge into computers (artificial intelligence) designed to model an ability to solve problems just like experts (Hendriani et al., 2021). The expert system helps laypeople to solve their problems or to look for the correct information from experts.

The Bayes Theorem Method was put forward by an English Presbyterian priest, Thomas Bayes, in 1763 and later refined by Laplace. The theorem is used to calculate the probability of an event occurring based on the influence from the observations (Fadhillah et al., 2021). Bayesian probability is one way to overcome data uncertainty by using the Bayes formula which is expressed by:

P(H |E); 1 y °

'Z2=iP (E|H*).P(H* )

Where: P(H |E): The probability of the hypothesis Hi occurring if evidence E occurs; P(E|H): The probability of evidence E to occur, if it is known that the hypothesis Hi occurs; P(H): Hi hypothesis probability regardless of any evidence; n: The number of hypotheses that occur.

RESULTS AND DISCUSSION

Data description from data collection became the alternative data in the calculation using the Bayes Theorem, as depicted in Table 1.

Table 1 - Data on Pests and Diseases

No. Names of Pests and Diseases Code

1. Rats P01

2. Whiteflies P02

3. Beetles P03

4. Fruit Borers P04

5. Garden Centipedes P05

6. Fruit Flies P06

7. Thrips P07

8. Scale P08

9. Root Rot Disease P09

10. Basal Rot Disease P10

11. Leaf Blight Disease P11

Table 2 - Symptoms of Pests and Diseases

No. Symptoms Code

1. The fruit has a wound, a sign of bites G01

2. The fruit has large holes and rots G02

3. The tips of the leaves curl, wither, and dry G03

4. Plants stop growing G04

5. The roots die and rot G05

6. The fruit looks hollow but not too big G06

7. The injured fruit secretes black sap and rots G07

8. The fruit has small holes G08

9. Fruit rot is followed by fungal or bacterial attacks G09

10. The plants become stunted G10

11. Pale leaves G11

12. Dead plants G12

13. The fruit looks watery, rotten, and soft G13

14. The leaves start to have silver spots G14

15. Slow plant growth G15

16. Small fruit size G16

17. Yellow striped leaves G17

18. The tips of the leaves are brown and dry G18

19. The leaves are easy to remove G19

20. Base rot with a brown rotting odor G20

21. Stem base, leaves, and fruit rot, with soft textures and brown color G21

22. The stems and leaves have white and yellowish patches G22

23. There are broad, round yellow spots on the leaves G23

24. Leaves are brown G24

Table 3 - The Probability Values of Pests and Diseases

Code of Pests and Diseases Code of Symptoms Score of Symptoms

P01 G01 0.5

G02 0.5

P02 G03 0.5

G04 0.25

G05 0.25

P03 G06 0.5

G07 0.5

P04 G08 0.66

G09 0.33

P05 G10 0.5

G11 0.25

G12 0.25

P06 G08 0.66

G13 0.5

P07 G14 0.5

G15 0.5

P08 G16 0.75

G15 0.25

P09 G17 0.4

G18 0.2

G19 0.2

G20 0.2

P10 G21 0.75

G22 0.25

P11 G23 0.5

G24 0.5

The following shows the calculation process using the Bayes Theorem method. Table 4 - Adding up the Probability Value of Each Evidence

Code of Pests and Diseases Names of Pests and Diseases n ^ = G1 + — + Gn k=n Results

P01 Rats G01 = P(E|H0i) = 0.5 G02 = P(E|HO2 ) = 0.5 2 ^ =0.5 + 0.5 = 1 fc=i 1

P02 Whiteflies G03 = P(E|H03) = 0.5 G05 = P(E|Ho5 ) = 0.25 2 ^ = 0.5 + 0.25 = 0.75 k=2 0.75

P05 Garden Centipedes G10 = P(E|H10) = 0.5 G12 = P(E|H12 ) = 0.25 2 ^ =0.5 + 0.25 = 0.75 k=5 0.75

P09 Root Rot G18 = P(E|H18) = 0.2 G19 = P(E|h19) = 0.2 G20 = P(E|H20) = 0.2 3 ^ = 0.2 + 0.2 + 0.2 = 0.6 k=9 0.6

The formula to find the H hypothesis probability without considering the evidence:

P(H) = P™

k

• P01 = Rats:

0.5

G01 = P(H01) = — = 0.5000

i

0.5

G02 = P(H02) = — = 0.5000

• P02 = Whiteflies:

0.5

G03 = P(H03) = — = 0.6667

0.75 0.25

G05 = P(H05) = = 0.3333

P05 = Garden Centipedes:

0.5

G10 = P(H10) = — = 0.6667

1 0.25

G12 = P(H12) = — = 0.3333

P09 = Root Rot:

0.2

G18 = P(H18) = — = 0.3333

0.6

0.2

G19 = P(H19) = — = 0.3333

0.6 0.2

G20 = P(H20) = 02 = 0.3333 Table 5 - Finding the Hi Hypothesis Probability Value

Code of Pests and Diseases Names of Pests and Diseases n ^ -P(H )* P(E|HJ + ••• + P(H3 )* P(E IH3 ) k-i Results

P01 Rats Sk -2 = (0.5000 * 0.5) + (0.5000 * 0.5) = 0.2500 + 0.2500 = 0.5000 0.5000

P02 Stem Borers Sk -2 - (0.6667 * 0.5) + (0.3333 * 0.25) = 0.3333 + 0.0833 = 0.4167 0.4167

P05 Garden Centipedes Sk -5 - (0.6667 * 0.5) + (0.3333 * 0.25) = 0.3333 + 0.0833 = 0.4167 0.4167

P09 Root Rot Sk -9 - (0.3333 * 0.2) + (0.3333 *0.2) +(0.3333 * 0.2) = 0.0667 + 0.0667 + 0.0667 = 0.2000 0.2000

The following shows the calculation process using the Bayes Theorem method:

P(E|H).P(H )

P(H|E)

Z5UP (E|Hfc).p(Hfc)

• P01 = Rats:

D/II ll=\ - 0.5 * 0.5000 _ _ cririri P(H01|E) = -- 05000

P(H02 |E) = = 0.5000

• P02 = Stem Borers:

P(H03 |E) = ^^f67 = 0.8000 P(H05 |E) = i^33 = 0.2000

P05 = Garden Centipedes:

p = 0.5 * 0.6667 = 0.8000

v 101 7 0.4167

p |E) = 0 25 * °.3333 = 0.2000

v 12 I ' f\ AA C.1

P09 = Root Rot:

p |E) = 0.2* 0.3333 = 0 3333

v 181 ' 0.2000

P(H19 |E) = 02* 03333 = 0. 3333 v 191 ' 0.2000

0 2 * 0 3333

P |E) = 02 03333 = 0. 3333 20 0.2000

Table 6 - Calculating the Total Value

Code of Pests and Diseases Names of Pests and Diseases n ^ Bayes = P(E|Hj) * P(H,|Et) + - + P(E|Hj) k=1 * P(H,|E,) Results

P01 Rats £2=1 bayes = (0.5 * 0.5000) + (0.5 * 0.5000) = 0.2500 + 0.2500 = 0.5000 0.5000

P02 Stem Borers £2=2 bayes = (0.5 * 0.8000) + (0.25 * 0.2000) = 0.4000 + 0.0500 = 0.4500 0.4500

P05 Garden Centipedes £2=5 bayes = (0.5 * 0.8000) + (0.25 * 0.2000) = 0.4000 + 0.0500 = 0.4500 0.4500

P09 Root Rot £2=9 bayes = (0.2 * 0.03333) + (0.2 * 0.03333) + (0.2* 0.03333) = 0.0667 + 0.0667 + 0.6666 = 0.2000 0.2000

From the calculation, it was found that the pest diagnosed was rats (100 * 0.5000 =

50%).

The following is the menu display created using the Bayes Theorem Method on an expert system. The menu display analyzes and proves whether each process is runninruns as expected.

When users access the website, the display of the main menu page will be as follows.

Figure 1 - The Display of the Main Menu Page

The following is the login display for the administrator to enter the main page. An administrator must fill in the username and password.

Figure 2 - Login Display A successful login will bring the administrator to the following display of the main menu.

Figure 3 - Main Menu Display for the Administrator

This page shows the symptom data. An administrator can add data related to the names and codes of symptoms. The administrator can also edit and delete the names and codes of symptoms.

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Bv)h t*(luWng bewr din m*mbuWl G&ltgfe XHipn

üjuriÄ ddun mcfen^uing. Upu dan ramjf Git* Kfep»

ttAKTWi bflWK» (Wiluiwftuft QTLlufa *Hifu.

Afcir mail din mcmbuuJt

Figure 4 - Symptom Data Page Display

This page shows the pest and disease data. An administrator can add data related to the names and codes of pests and diseases. The administrator can also edit and delete the names and codes of pests and diseases.

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PtfQgvnjun Mruh Htm m^KJiW! fl*fi MWW) MM ¿¡¿r (Kl* GLtih

IJWÎUI* WHjpîi

Figure 5 - Pest and Disease Page Display

This page contains the base for making rules calculated using the Bayes Theorem. An administrator can add the pest and disease codes and the solutions to the problems of pets and diseases. The admin can also change and delete the data.

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KHjpui KHjfti

Figure 6 - Rule Page Display The page shows the report from user consultation activities.

^ Admin BPP. Pertanian Kecamatan PamatangSilimahuta

Oata Uporari User

Figure 7 - User Report Page Display

The page of pest and disease info displays information about pests and diseases of pineapple plants.

H SPP. Peitanian Kecamatan Pa matang Silimahuta

Info Hama dan Penyakic Pada Tana man Nanas

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1. Tftia lliaiui Mgmhmm

2. KuÇu Putili CCVwfcoccus B«\ip«>

6. LSJt Bush pUtwigDftj ÎP1

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8. SJsA (Dusgft bromrt« Kerne)

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Figure 8 - Pet and Disease Info Page Display

This page is for users to fill in their data before proceeding to the diagnosis.

Figure 9 - User Data Page Display The page helps users choose the symptoms they find in their pineapple plants.

2 BPP. Pertanian Keeamatan Pamatang Sihmahuta

Figure 10 - Symptom Selection Page Display The page displays the results of the diagnosis.

2 BPP. Pertanian Keeamatan Pamatang Silimahuta

Hasil Diagnosa Hama dan Penyakit Tanaman Nanas

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Figure 11 - Diagnosis Results Page Display

The page displays the report of diagnosis results done by users.

Figure 12 - Diagnosis Result Report Page Display CONCLUSION

Based on the findings and discussion, the web-based developed expert system using the Bayes Theorem shows accurate results for diagnosing pests and diseases of pineapple plants. The system helps farmers to effectively and efficiently diagnose pests and diseases in their plants. The system was developed using an expert system employing visual studio code and a web-based system to ease access anywhere and anytime.

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