1. Scenario: Coconut Yield Management
FCM WIZARD IN ACTION
Scenario: Coconut Yield Management
Main developers: Gonzalo NÁPOLES and Isel GRAU
Supervisors: Elpiniki PAPAGEORGIOU and Koen VANHOOF

2. Scenario description
The present study case is focused on categorizing the coconut production level for the given set of agro-climatic conditions using the methodology of fuzzy cognitive map (FCM) enhanced by its learning capabilities. Additionally, an attempt is made to study the impact of climatic variations and weather parameters on the coconut yield behavior using the reasoning capabilities of FCM.
Concepts represent various soil conditions as well as the seasonal and weather parameters that are believed by three experts to influence the productivity level of coconut.
Decision concept represents the coconut yield category.
Dataset source: Real coconut field data of different seasons for the period from 2009 to 2013 of Kerala
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

3. Soil electric conductivity C13: Mn Manganese level in the soil C2: OM
Concept
Description
C1: EC
Soil electric conductivity
C13: Mn
Manganese level in the soil
C2: OM
Organic matter present in the soil
C14: Fe
Iron level in the soil
C3: BD
Bulk density of the soil
C15: Zn
Zinc level in the soil
C4: Temp
Atmospheric temperature
C16: Mg
Magnesium level in the soil
C5: Hum
Humidity
C17: Cu
Copper level in the soil
C6: Pest
Percentage infection of pest to the plant
C18: K
Potassium level in the soil
C7: AoP
Age of plants
C19: P
Phosphorus level in the soil
C8: RF
Annual rainfall amount
C20: N
Nitrogen level in the soil
C9: RFD
Annual rainfall duration
C21: Yield
Annual coconut yield per plant
C10: SM
Surface soil moisture
C11: pH
pH level of soil
C12: Ca
Calcium level in the soil
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

4. Construction of the causal weights
Experts describe each interconnections with linguistic fuzzy rule to assign the weight. The fuzzy rules for each interconnection can be calculated using the following:
IF a change 𝐵 occurs in the value of concept 𝐶 𝑗 THEN a change 𝐷 in the value of concept 𝐶 𝑖 is caused. It must be concluded that the influence from 𝐶 𝑗 to 𝐶 𝑖 is 𝐸. In this representation rule B, D and E are fuzzy linguistic variables that experts should specify.
Example.
IF a small change occurs in the value of C22 THEN a small change is caused in the value of OUTC1. It must be concluded that the influence of C22 to OUTC1 is low.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

5. Designing the FCM model
Create a new map by using the menu File |New Map. The canvas is immediately cleaned and ready to be used in a new map.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

6. Designing the FCM model
Add concepts and relationships between them by drag and dropping from the source concept to the target one.
During the modeling phase the designer write the value of each causal weight.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

7. Adaptation of causal weights
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

8. Adaptation of causal weights
The tool includes unsupervised and supervised learning algorithms. Set up the parameters for the chosen algorithm and browse for the historical dataset source (in ARFF format).
In this study case we prefer to use an unsupervised learning approach for adapting the parameters since the initial modeling must be considered.
However, using an evolutionary approach we cannot ensure small variations of the initial modeling and therefore the system could be difficult to explain.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

9. Adaptation of causal weights
To adjust the matrix for an input vector corresponding with low yield of coconuts. We use an activation vector designed by experts, corresponding to the desired decision class.
Final response for the activation vector A1 = {0.8, 0.075, 0.566, 0.725, 0.7, 0.67, 0.75, 0.084, , 0.36, , 0.07, , 0.745, 0.514, 0.101, , 0.261, 0.136, 0, 6, 0}
Analogous we can adjust the causal relations for classes high yield and medium yield scenarios.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

10. FCM Inference
Set initial values for concepts and trigger the inference Run | Run Inference Process. Next a plotter will be showed illustrating the inference process in detail.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

11. Interpretation of the modeled system
In this case study, the FCM mimics the experts’ belief regarding to the effect of monsoon rainfall variability, the effect on summer showers and the effect of maximum temperature. The predictions for the presented example is well in accordance with the traditional expert belief about coconut yield behavior with respect to seasonal effects and climatic changes.
In addition to the weather-related parameters, the causal associations of different soil parameters, including the three primary nutrients (N, P, K) and other secondary/micro nutrients, are also crucial for coconut yield prediction and categorization. The analysis of soil composition and its influence over yield gives a hint to the farm officers and other stakeholders about the impending outcome, thus giving them indications about the manipulations needed in the farm management and cultivation practices to be followed for yield improvement. For instance, the amounts of P, N, K, Ca, Mn, etc., can be modified by fertilizer applications. Similarly, the amount of organic matter may be also be adjusted appropriately by means of applying organic manure.
See reference above for more information on the case study.
L. S. Jayashree, Nidhil Palakkal, Elpiniki I. Papageorgiou, Konstantinos Papageorgiou, Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region, Neural Comput. & Applic., 26, (2015).

12. Scenario: Coconut Yield Management
FCM WIZARD IN ACTION
Scenario: Coconut Yield Management
Main developers: Gonzalo NÁPOLES and Isel GRAU
Supervisors: Elpiniki PAPAGEORGIOU and Koen VANHOOF