Jane Labadin, Kartinah Zen, Emmy Dahliana Hossain and Chen Shyang Ren Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak

Introduction Bario is famous for its fragrant rice which is the best among others and higher price. Paddy Yield Prediction Tool proposed to assist the villagers who involved in growing Bario rice. Most of the existing prediction tools are developed for big agricultural land for food industries but not rural villages. Low cost Wireless Sensor Networks (WSN) are integrated for paddy monitoring and data gathering.

Requirements and Assumptions Measurement  Weight measurement in Kilograms (Kg)  Land measurement in arces. Paddy Planting Activities MonthActivities Mid to end of JuneCreate nursery using damp gunny sacks (semaian) AugustMove the young paddy plant into the field End of December to Early January Harvest the paddy

Consists of 4 components that are WePreT, ETo Calculator, AquaCrop and PaYPreT. WePreT (Weather Prediction Tool)  Supply data required for other component.  Received data from WSN that are,  Minimum and Maximum Temperature (Celsius, ℃ )  Relative Humidity (%)  Light Intensity (hours)  Rainfall (mm)  Prediction of the data will be done for the next 1 or 2 years.

ETo Calculator & AquaCrop  Free software that developed by Land and Water Division of Food and Agricultural Organization (FAO).  ETo Calculator is used to calculate evapotranspiration (ETo) which is one of the inputs for AquaCrop.  AquaCrop will generate the yield by part by all the inputs from WePreT and ETo Calculator. PaYPreT  It will convert the yield by part from AquaCrop into the units understood by users.  The yield will be calculated by multiplication of the yield by part with the area inputted by the user.

Sample GUI for PaYPreT

Proposed Hardware Design

Sensors in WSN integrated with camera, humidity, light intensity, temperature and water level sensing. The system is later on extends on deploying and specifying on data transfer protocol. Few sensors will be positioned in the paddy field, where one sensor will cover about 100 meters radius. Images of the paddy will be taken from day to day and save in server’s database for paddy growth monitoring. The reading of water level by the sensors will be used for water irrigation control.

Conclusion Paddy yield prediction tool will let the farmer know the maximum yield with the maximum usage of the land in farming. It’ll also motivate paddy plantation in massive amount and attract involvement of young generations. A lot of farming problem can be reduced by using WSN.

Thank You.