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.

Efficient biogas production significantly impacts greenhouse gas (GHG) emissions and carbon sequestration by reducing emissions and enhancing carbon storage. Nonetheless, the consistency and optimization of biogas production are hindered by fluctuations in key input variables, namely, pH, moisture content, organic loading rate (OLR), and temperature, which significantly impact the quality of agricultural waste biomass and biogas production. Any fluctuations in these variables can affect biogas productivity. This study aims to provide valuable optimization parameters for maximum biogas production using rice straw and cow dung as agricultural waste biomass for biogas input materials. Therefore, machine learning techniques such as multilayer perceptron feedforward neural networks with a particle swarm optimization (PSO) combination generate optimal values for each variable for maximum biogas production. This study uses three variants of the training function for neural networks, namely gradient descent with momentum and adaptive learning rate, gradient descent with momentum, and gradient descent with adaptive learning rate. The findings reveal that, under an optimum pH value of 6.0000, a humidity of 62.3176%, an OLR of 67.6823 kg.m³/day, and a temperature of 37.0482 °C, biogas production has the potential to increase to 2.91 m³/day with a high accuracy testing value of R² = 0.90. These methods in use accurately predict the optimal parameters, with a maximum deviation of 8.48% from experimentally derived values and mean square error (MSE) of 0.0051243. This study emphasizes the benefits of using multilayer perceptron feedforward neural networks and particle swarm optimization to optimize operational parameters and accurately predict biogas production.

This study was aimed to examine the appropriate and effective techniques for tapping and sheltering coconut sap to produce quality crystal coconut sugar products according to SNI 3743-2021 for palm sugar. The research was conducted using an experimental method with a non-factorial randomized block design (RBD) of 14 different tapping and shelter treatments. The physicochemical parameters of crystal coconut sugar that were observed were moisture content, ash content, sucrose and reducing sugar content. The statistical analysis of data using the ANOVA test (F test), DMRT α 5% test and the Effectiveness Index Test. The results showed the five best methods for tapping and sheltering coconut sap that could produce crystal coconut sugar products with quality levels of sucrose, reducing sugar, moisture and ash content according to SNI 3743-2021, respectively are methods VII, V, VIII, VI and I. Method VII was tapping in from the afternoon to night for 8 hrs (14.00 AM-22.00 PM), + 2% laru (natural preservative), without heating and shelter duration or being processed directly into crystal coconut sugar. Method V was tapping in the morning to noon for 8 hrs (06.00-14.00 AM), + 2% laru (natural preservative), without heating shelter duration. Method VIII was tapping afternoon to night for 8 hrs (14.00 AM22.00 PM), + 2% laru (natural preservative), without heating, and shelter duration for 3 hrs. Method VI was tapping in the morning to noon for 8 hrs (06.00-14.00 AM), + 2% laru (natural preservative), without heating and shelter duration for 3 hrs. Method I was tapping in the morning to noon for 6 hrs (06.00-12.00 AM), without laru (natural preservative), without heating and shelter duration or being processed directly into crystal coconut sugar.