Life cycle assessment of integrated microalgae oil production in Bojongsoang Wastewater Treatment Plant, Indonesia.

This study aims to determine the eco-friendliness of microalgae-based renewable energy production in several scenarios based on life cycle assessment (LCA). The LCA provides critical data for sustainable decision-making and energy requirement analysis, including net energy ratio (NER) and cumulative energy demand (CED). The Centrum voor Milieuwetenschappen Leiden (CML) IA-Baseline was used on environmental impact assessment method by SimaPro v9.3.0.3® software and energy analysis of biofuel production using native polyculture microalgae biomass in municipal wastewater treatment plants (WWTP) Bojongsoang, Bandung, Indonesia. The study was analyzed under three scenarios: (1) the current scenario; (2) the algae scenario without waste heat and carbon dioxide (CO2); and (3) the algae scenario with waste heat and carbon dioxide (CO2). Waste heat and CO2 were obtained from an industrial zone near the WWTP. The results disclosed that the microalgae scenario with waste heat and CO2 utilization is the most promising scenario with the lowest environmental impact (- 0.139 kg CO2eq/MJ), positive energy balance of 1.23 MJ/m3 wastewater (NER > 1), and lower CED value across various impact categories. It indicates that utilizing the waste heat and CO2 has a positive impact on energy efficiency. Based on the environmental impact, NER and CED values, this study suggests that the microalgae scenario with waste heat and CO2 is more feasible and sustainable to adopt and could be implemented at the Bojongsoang WWTP.

Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN.

In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12-2 PM), low-light (5-6 PM), and no-light (7-8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions.

Effect of Raman exposure time on the quantitative and discriminant analyses of carotenoid concentrations in intact tomatoes.

The significant worldwide expansion of the health food market, which includes functional fruits and vegetables, requires a simple and rapid analytical method for the on-site analysis of functional components, such as carotenoids, in fruits and vegetables, and Raman spectroscopy is a powerful candidate. Herein, we clarified the effects of Raman exposure time on quantitative and discriminant analysis accuracies. Raman spectra of intact tomatoes with various carotenoid concentrations were acquired and used to develop partial least squares regression (PLSR) and partial least squares discriminant analysis (PLS-DA) models. The accuracy of the PLSR model was superior (R2 = 0.87) when Raman spectra were acquired 10 s, but decreased with decreasing exposure time (R2 = 0.69; 0.7 s). The accuracy of the PLS-DA model was unaffected by exposure time (hit rate: 90%). We conclude that Raman spectroscopy combined with PLS-DA is useful for the on-site analysis of carotenoids in fruits and vegetables.

A Novel Polyculture Growth Model of Native Microalgal Communities to Estimate Biomass Productivity for Biofuel Production.

Native polyculture microalgae is a promising scheme to produce microalgal biomass as biofuel feedstock in an open raceway pond. However, predicting biomass productivity of native polycultures microalgae is incredibly complicated. Therefore, developing polyculture growth model to forecast biomass yield is indispensable for commercial-scale production. This research aims to develop a polyculture growth model for native microalgal communities in the Minamisoma algae plant and to estimate biomass and biocrude oil productivity in a semi-continuous open raceway pond. The model was built based on monoculture growth of polyculture species and it is later formulated using species growth, polyculture factor (k value ), initial concentration, light intensity, and temperature. In order to calculate species growth, a simplified Monod model was applied. In the simulation, 115 samples of the 2014-2015 field dataset were used for model training, and 70 samples of the 2017 field dataset were used for model validation. The model simulation on biomass concentration showed that the polyculture growth model with k value had a root-mean-square error of 0.12, whereas model validation provided a better result with a root-mean-square error of 0.08. Biomass productivity forecast showed maximum productivity of 18.87 g/m2 /d in June with an annual average of 13.59 g/m2 /d. Biocrude oil yield forecast indicated that hydrothermal liquefaction process was more suitable with a maximum productivity of 0.59 g/m2 /d compared with solvent extraction which was only 0.19 g/m2 /d. With satisfactory root mean square errors less than 0.3, this polyculture growth model can be applied to forecast the productivity of native microalgae. This article is protected by copyright. All rights reserved.

Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach.

Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.

Simple and rapid determination of free fatty acids in brown rice by FTIR spectroscopy in conjunction with a second-derivative treatment.

A simple and rapid method for the determination of free fatty acid (FFA) content in brown rice using Fourier transform infrared spectroscopy (FTIR) in conjunction with second-derivative treatment was proposed. Ground brown rice (10g) was soaked in toluene (20mL) for 30min, and the filtrate of the extract was placed in a 1mm CaF2 liquid cell. The transmittance spectrum of the filtrate was recorded using toluene for the background spectrum. The absorption band due to the CO stretching mode of FFAs was detected at 1710cm(-1), and the Savitzky-Golay second-derivative treatment was performed for band separation. A single linear regression model for FFA was developed using the 1710cm(-1) band in the second-derivative spectra of oleic acid in toluene (0.25-2.50gL(-1)), and the model displayed high prediction accuracy with a determination coefficient of 0.998 and a root mean square error of 0.03gL(-1).