The efficiency of photosynthesis in strawberry plants is measured to maintain the quality and quantity of strawberries produced. The latest method used to measure the photosynthetic status of plants is chlorophyll fluorescence imaging (CFI), which has the advantage of obtaining plant spatiotemporal data non-destructively. This study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm). The main components of this system include a chamber for plants to adapt to dark environments, blue LED light sources to excite the chlorophyll in plants, and a monochrome camera with a lens filter attached to capture the emission spectra. In this study, 120 pots of strawberry plants were cultivated for 15 days and divided into four treatment groups: control, drought stress, heat stress, and a combination of drought and heat stress, resulting in Fv/Fm values of 0.802 ± 0.0036, 0.780 ± 0.0026, 0.768 ± 0.0023, and 0.749 ± 0.0099, respectively. A strong correlation was found between the developed system and a chlorophyll meter (r = 0.75). These results prove that the developed CFI system can accurately capture the spatial and temporal dynamics resulting from the response of strawberry plants to abiotic stresses.

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.

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.

Nowadays, various affordable sensors and devices have been developed and utilized in a wide area of sectors. Needless to say, agriculture sector is no exception. They have already been applied to improve agricultural production in terms of advancing the knowledge and techniques of farming, reducing production costs, and improving the quality of agricultural produce. This paper introduces the current situation and example of the affordable sensors and devices for field environment monitoring and plant growth measurement in smart agriculture.

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.