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Agriculture in Indonesia has recently experienced challenges, especially in terms of meeting market demand that tends to increase. Precision agriculture is an effort to increase productivity through optimizing agricultural production processes by minimizing inputs, maximizing output, and reducing the impact on the environment through the use of information and communication technology. Implementation of the concept of precision agriculture is in observing plant growth and development to estimates of plant volume and mass. So far, conventional plant measurements by direct measurement can pose destructive risks to plants. Therefore, a non-destructive system for observing and measuring plants is needed. The purpose of this study was to develop a plant growth observation system based on three-dimensional modelling with non-destructive measurements using the Close-Range Photogrammetry (CRP) method. This system has a 360 orbital scanner component that consists of a frame, camera arm, NEMA 17 stepper motor, Logitech C270 web-camera, Raspberry Pi 3 B +, Adafruit motor driver, and LED strip lights. The stage of image processing using 3DF Zephyr Pro software for generating the 3D sparse point cloud and cropping the unused point. For the validation purposes and its functionality for modelling and estimating volumetric objects, a plant model with three sizes (small, medium, and large) variations were used. As the result, the developed system could observe and model the plant shape in a three-dimensional manner resemble the actual plant model.

Precision agriculture (PA) is an approach in farming systems based on information and technology to enhance agricultural production. PA is applied in plant factories to produce high-quality vegetables in a controlled environment by implementing an automatic crop growth monitoring system. Optimization of the observation process carried out with computer system techniques used in the monitoring process and assessment of the plant condition. Individual observation of cultivated plants requires a monitoring system and numerous equipment. Therefore, a monitoring system equipped with a mobile mechanism is needed to support the monitoring process of crops individually by a single observer. The objective of this study was to design and implement an automatic plant monitoring system based on mobile mechanisms and computer vision using simple equipment in plant factories. The Mobile Mecavision, a monitoring system consists of a mobility system and an image acquisition system controlled by Raspberry Pi. The components are A4988, NEMA 17, and Logitech C270 HD Webcam. The evaluation for system performance carried out using the extreme point shift system and t-test. The aspect of image functionality evaluated using the Structural Similarity Measure test and the result was obtained an image similarity value of 89% to 92% with a 90% level of confidence. The result of the performance test on the mobility aspect obtained that the system shifted numerically but not statistically. The overall performance of the system indicated that the monitoring system could be applied to monitor the crop growth in plant factories.

One of the applications of precision agriculture is the monitoring of plant growth in a plant factory production to observe the behavior and predict the estimated yield of plant production. Plant growth is unique and is affected by internal and external factors, such as environmental conditions and nutrition supply. The estimation of plant growth considering the environmental conditions as well as initial plant height is necessary for plant management during the production cycle. Therefore, to answer the challenge, the purpose of this study was to develop a model of plant growth prediction using the resilient backpropagation Artificial Neural Network (ANN) method with environmental parameter input at the plant factory and evaluate the model. The ANN model was tested using a different number of nodes at the hidden layer, which are 1 to 7 nodes with the input of daily average temperature, average daily humidity, EC, and light intensity and then produces high lettuce increase output for 45 days. The model was developed and tested using the lettuce (Lactuca sativa) in plant factory production. As a result of the evaluation, the best prediction model with ANN is using the network architecture 4-7-1 with the results of the interpretation of R2 on the training data, and testing data are 0.987 and 0.728. From the verification test of the developed model, it can be found that the most affecting way to optimize lettuce growth is the rate of EC in nutrition. The results of the RMSE model validation is 0.032. Accordingly, the developed model can be used to predict the height increase of Lettuce (Lactuca sativa) plants in a plant factory.

To support daily farm management through the environmental challenges, it is necessary to have short-term evapotranspiration forecasting to predict an n-hour step. 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 behaviour in open-field cultivation. The objective of this study was to develop a short-term evapotranspiration forecasting model using the time series method. The model is based on the Seasonal Autoregressive Integrated Moving Average (SARIMA). The environmental data of air temperature, relative humidity, and solar radiation were observed at Rejeki Tani Yogyakarta from January to August 2014. The ET was estimated using the FAO56 Penman-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)s were investigated to predict 12-hour ahead of ET. As the result, the suitable parameter was SARIMA(2, 1, 2)(1, 1, 1)24. From the six months model validation with the different monsoons, the MAE and RMSE ranged from 0.035636 to 0.063419, and 0.045893 to 0.079961 respectively. The R2 was between 0.8045 and 0.85902. The developed forecasting model can be used to predict the hourly evapotranspiration with acceptable error and accuracy.

The plant factory is extensive cultivation to produce a high quality of vegetables under a controllable environment. The concept of Precision Agriculture (PA) was introduced to improve the plant factory production by the implementation of a crop growth monitoring system. Crop growth can be estimated by monitoring of crop height and canopy foliage by the use of computer vision technology. In our previous study, we have introduced a plant height monitoring system based on depth perception using a stereo camera. However, the validity of the various type of leave is necessary to be tested. The objective of this study was to implement the crop growth monitoring system to monitor plant development with various type of leave for system validation and evaluation. The crop growth monitoring system composed of a stereo camera implementing the depth perception for estimating the distance from camera to highest point in the crop. The implementation of the system with various types of leaves and characteristics has been conducted for (a) Samhon, (b) Lettuce, and (c) Pagoda. The developed crop growth monitoring system could perform the time series estimation of crop height with a maximum error of RMSE 0.875 cm on Pagoda, and MAPE of 5.56% on Lettuce. The system demonstrates better estimation on Samhong with minimum error RMSE of 0.408cm and 2.27 % of MAPE. Overall validation for the estimated height vs actual measurement indicates that the coefficient of determination higher than 0.7 means that it has substantial features for estimating the plant height.

Circadian rhythm is a biological rhythm in a plant that has a 24-hour period, which affects the plant activities such as metabolism processes, physiology processes, and plant behavior. Circadian rhythm represents the biological clock that entrained by the environmental condition affected by the earth rotation. As an alternative to monitoring the circadian rhythm in the plant was the use of leaf motion as a physical indicator. The objective of this study was to present the quantitation method for leaf movement to study the circadian rhythm using the optical flow method. The leaf movement was analyzed from the captured time-lapse imaging using an Infra-red camera to capture the day-night movement of the leaf of Chili (Capsicum annum L.) from top and side view projection. As a result, the quantification method could quantify the leaf motion of Chili for both top and side view projection with the higher movement observed at top view projection. The quantified motion could show the diurnal pattern of circadian rhythm clearly and will be used for further investigation related to plant behavior in response to environmental changes.

Tropical agriculture production is profoundly affected by the uncontrollable environmental condition. To obtain good agricultural products, farmers manage their farm to fit with the variation of weather and season. The farming managements are generally determined according to farmers personal knowledge, gained from the long-term experience from preceding years. Nowadays, climate change intensifies unpredictable weather and its unstable distribution. Consequently, conventional farming management considering the climate change factor should be considered. The farming management should face the dynamic change of weather condition as well as improve the farmer’s knowledge towards the implementation of precision agriculture approach. The objective of this study was to introduce an appropriate adaptation of precision agriculture technology by the utilization of information and communication technology (ICT) to improve conventional farming management in tropical agriculture production. The framework is implementing cloud technology as a backbone, which can be extended with various knowledge such as data science, plant biology, plant physiology, biophysical, and biomechanical. The appropriate adaptation of precision agriculture represented by the improvement of the conventional farming method using the technological aspect by fostering their knowledge to adopt modern agriculture empowered with Information and Communication Technology (ICT). Learning process from data-information-knowledge on the application of precision agriculture will be documented at the PA Knowledge Management System.

Wireless Sensor Network (WSN) is widely used in field environmental monitoring recently, because of its ease of use and being able to simplify the complexity installation in real application. Indonesia is an agricultural country with more than 16.5 million of farming land, most of the farming land is located in a rural area. The problems on the implementation of IoT-based environmental monitoring system in a rural area is the limited signal and energy. Accordingly, the objective of this study was to develop an Environmental Monitoring System based on LoRa in a rural area with the implementation of the Local Management Subsystem (LMS) and Global Management Subsystem (GMS) framework to optimize the existing development of the Environmental Monitoring System with focused on optimum distance by measuring the RSSI, signal strength and packet loss. The research was conducted in Bulaksumur Universitas Gadjah Mada, Yogyakarta, with four zones and various obstacles. As the result, Zone D with line of sight was the best result for getting the affordable distance for data transmission. This Zone can reach over 800 m distance with only 20% packet loss.