Development of Short-term Evapotranspiration Forecasting Model using Time Series Method to Support The Precision Agriculture Management in Tropics

Open field tropical horticulture production is highly affected by the uncontrollable environment. Consequently, farmer manages their farming activity so as to adapt the environment, such as an appropriate planting schedule, plant maintenance, and daily irrigation. Nowadays, climate change intensifies unpredictable weather and unstable climate distribution. The precision farming approach was introduced by the utilization of on-site environmental monitoring system to support the decision-making process for the daily operations. Evapotranspiration (ET) is the sum of evaporation and transpiration from the soil surface and plant tissue that can be used to assess the water loss behavior in open- field cultivation. In order to support the daily farm management, it is necessary to have a short-term evapotranspiration forecasting to predict n-hour step ahead. The objective of this study was to develop a short-term evapotranspiration forecasting model using time series method. The model is based on Seasonal Autoregressive Integrated Moving Average (SARIMA). The environmental data of air temperature, relative humidity, and solar radiation, observed at Rejeki Tani Yogyakarta on January to August 2014, were used for the datasheet. The ET was estimated using the FAO56 Penmann-Monteith. A suitable parameter of non- seasonal autoregressive order (p), the degree of differencing (d), moving average order (q), and their seasonal parameter (P, D, Q)m were investigated to predict 12-hour ahead of ET. As the result, the suitable parameter was SARIMA (1,2,1)(0,2,1)24. From the 8 days model verification, maximum MSE and RMSE were 0.069 and 0.091 respectively. From the model validation with the different monsoon, the coefficient of determination (R2) was 0.955.

Author:  Andri Prima Nugroho, Dita Endah Rahayu, Lilik Sutiarso, Mohammad Affan Fajar Falah, and Takashi Okayasu.

This paper will be presented at the National Conference of Indonesian Society of Agricultural Engineer 2018 in Aceh, Indonesia.

Automatic Leaf Motion Analysis Using Optical Flow To Diagnose Plant Behavior In Response To Environmental Changes

Abstract: We have proposed an automatic leaf motion analysis based on Infrared (IR) photography. The proposed system allows continuous, high-resolution time-lapse imaging, independent of the present of visible light, and based on open source platform. The utilization of microcomputer as a capturing unit might increase the simplicity and flexibility. The IR camera positioned above the plant as top view projection to capture the foliage images. As the motion tracking, the Optical Flow method was adopted, implementing the Lucas-Kanade technique with Shi-Tomasi corner detection. Optical flow is one of the motion detection methods to analyze the motion between two consecutive images by differentiating the constraint. Displacement distance and direction angle were used as a parameter for evaluation with regards to circadian rhythm. As the result, the proposed automatic leaf motion analysis could perform a continuous IR time-lapse photography and analyze the lateral 2D motion using the Optical Flow method. The highest probability of displacement distance observed at 6px (48%) and the direction angle was at 180° (39.2%). According to the visual appearance of time series data, the motion displacement seems to have a regular pattern resembles the circadian rhythm with regards to environmental changes although the noise also appears. To verify the relation between displacement distance, motion angle and circadian rhythm, analysis of phase and amplitude of data using a Fast Fourier Transform Nonlinear Least Squares (FFT-NLS) method will be in our future works.

Key Words: Plant motion, optical flow, leaf motion, circadian rhythm, motion analysis

Study on Plant Motion Measurement and Analysis Utilizing Computer Vision: Development of Automatic Plant Motion Analysis using Optical Flow

Plants, like other living things, have biological clocks that allow them to respond the changes in time and environment1). At day, plants do photosynthesis to produce carbohydrate such as sugar. While at night, plants distribute the stored the carbohydrate to grow leaves, stem flowers, fruits, and roots. Analysis of plant growth and motion at night might be used as an indicator of plant response due to environmental change and conditions.The objective of this study was to develop an automatic plant motion analysis with machine vision applying optical flow technique. The night photos were captured with each 30 min. interval by using an infrared camera (Pi NoIR) installed in the Raspberry Pi B+, running the Raspbian Whezzy 3.18 and infrared LED light. This capturing device was located at 1 m above the plant. The plant motions were analyzed by using the optical flow method in the OpenCV libraries. The experiment was conducted in a tomato (Solanum lycopersicum) house at the Kyushu University. Optical flow is one of the motion detection methods to analyze the motion between two consecutive images. Assuming an object pixel at time t, the same intensity part was found from the image at a time after the motion. Differentiating the constraint gives the following equation, where are the motion vector and the time derivative. The distance and direction of motion will be used as the indicator for plant diagnosis in relation with environmental data.

Oral presentation at The Joint Conference on Environmental Engineering in Agriculture 2015. Iwate University, Morioka, Japan.


Development of Smart Cloud-based Irrigation Controller: Real-time Evapotranspiration Monitoring in Tomato Greenhouse

The smart irrigation controller is a controller that reduces water use by monitoring and utilizing site environmental information and applying the right amount of water. These controllers receive feedback from the irrigated system and adjust irrigation duration and/or frequency accordingly. There are generally two types of smart controllers: climatologically based and soil moisture-based. Climatologically based controllers are also known as evapotranspiration or ET-controller. Evapotranspiration is the process of transpiration by plants combined with evaporation that occurs from plant and soil surfaces. In order to take the advantages of both type of controllers, the objective of this study is to present the development of hybrid soil moisture sensor and climatologically based controller. As part of the development of complete smart irrigation controller, real-time hourly step evapotranspiration monitoring is required on the continuous way of control. For that reason, the objective of this study was to develop a real-time evapotranspiration monitoring in the application of smart cloud-based irrigation controller.The developed real-time evapotranspiration monitoring could estimate the hourly step reference evapotranspiration (ETo) from the environmental data. The real-time computation performed in an executable function that triggered every 10 min. by implementing time shifting. Investigation on the relation between soil moisture and ETo as a potential input of hybrid controller for the smart irrigation controller is in our future works.

Poster presentation at The Kyushu Branch Japanese Society of Agricultural Machinery and Food Engineers Conference 2015
Kagoshima University, Kagoshima, Japan.

Extended Abstract


Example of Latex page

At first, we sample $f(x)$ in the $N$ ($N$ is odd) equidistant points around $x^*$:
f_k = f(x_k),\: x_k = x^*+kh,\: k=-\frac{N-1}{2},\dots,\frac{N-1}{2}
where $h$ is some step.
Then we interpolate points $\{(x_k,f_k)\}$ by polynomial
\begin{equation} \label{eq:poly}
Its coefficients $\{a_j\}$ are found as a solution of system of linear equations:
\begin{equation} \label{eq:sys}
\left\{ P_{N-1}(x_k) = f_k\right\},\quad k=-\frac{N-1}{2},\dots,\frac{N-1}{2}
Here are references to existing equations: (\ref{eq:poly}), (\ref{eq:sys}).
Here is reference to non-existing equation (\ref{eq:unknown}).

Research on Agricultural Informatics – Utilization of ICT to Improve The Agricultural Production System in Tropical Country


Environmental conditions is important factor affects the agricultural production system. In four seasons country like Japan, weather condition during cultivation can be fully controlled by utilization of greenhouse and environmental management. Microclimate manipulator can be managed automatically based on environmental evaluation. However, in tropical country like Indonesia, utilization of automatic control on greenhouse for agricultural production system is still rare because of the operational costs and the economic aspects. Thus, here we try to utilize the Information and Communication Technology (ICT) to improve the agricultural production system on tropical agriculture by implementation of regular environmental measurement using Field Monitoring System (FMS), Real-time environmental evaluation and information processing, and provision of decision support on farming activities. We realize that the incorporation of  farmer’s knowledge and utilization of ICT would be able to support modern agriculture and also promising  the progress on towards precision agriculture.