Land Suitability and Insurance Premiums: A GIS-based Multicriteria Analysis Approach for Sustainable Rice Production

The purpose of this research is to develop a land suitability model for rice production based on suitability levels and to propose insurance premiums to obtain maximum returns based on the harvest index and subsidy dependence factor for the marginal and moderately suitable lands in the northern part of Bangladesh. A multicriteria analysis was undertaken and a rice land suitability map was developed using geographical information system and analytical hierarchy process. The analysis identified that 22.74% of the area was highly suitable, while 14.86% was marginally suitable, and 28.54% was moderately suitable for rice production. However, 32.67% of the area, which was occupied by water bodies, rivers, forests, and settlements, is permanently not suitable; 1.19% is presently not suitable. To motivate low-quality land owners to produce rice, there is no alternative but to provide protection through crop insurance. We suggest producing rice up to marginally suitable lands to obtain support from insurance. The minimum coverage is marginal coverage (70%) to cover the production costs, while the maximum coverage is high coverage (90%) to enable a maximum return. This new crop insurance model, based on land suitability can be a rational support for owners of different quality land to increase production

Environmental load assessment for an integrated design of microalgae system of palm oil mill in Indonesia

The environmental load of continuous bioenergy production from palm oil (Elaeis guineensis) included with a proposed 10 ha of microalgae production system were assessed to be implemented in Indonesia. Material and energy balances, greenhouse gas (GHG) emission, nutrient requirement and also water scarcity during bioenergy production cycle were evaluated. The integrated system was developed for 60 tons h−1 of fresh fruit bunch (FFB) processing capacity of a conventional mill. Aggregate of energy-profit ratio from the proposed system was 5.20, which indicates a positive balance. The total water footprint for each palm oil and microalgae cultivation was 3.18 and 2.85 m3 kg−1 of biodiesel production, respectively. Microalgae mix-culture has the potential to treat organic compounds from palm oil mill effluent (POME) and combined with flue gases from biomass and biogas power plant as the alternative nutrient sources contributed to net-reduction of GHG emission for 158.8 tons ha−1 of microalgae culture, annually. The integrated system produced 26,471 tons of biodiesel that included 223 tons from microalgae and contribute to 39.90% of total GHG emission reduction from diesel fuel substitute. Additional co-product of 520.33 tons year−1 of animal feed from defatted biomass also possible to be produced and have potential for environmental benefits

Engineering Study of a Pilot Scale Process Plant for Microalgae-Oil Production Utilizing Municipal Wastewater and Flue Gases: Fukushima Pilot Plant

This article presents an engineering study of an integrated system to produce bio-oil from microalgae biomass. The analysis is based on a pilot plant located at Minami-soma Fukushima, Japan, which further simulates 1 ha based-cultivation. Municipal wastewater and flue gases were utilized as nutrient sources for the microalgae culture of the proposed design. A flow sheet diagram of the integrated plant was synthesized by process engineering software to allow simulation of a continuous system. The design and sizing of the process equipment were performed to obtain a realistic estimation of possible production cost. The results demonstrated that nutrient savings was achieved by wastewater and CO2 utilization to the polyculture of native microalgae. Process simulation gave an estimated CO2 sequestration of 82.77 to 140.58 tons ha−1year−1 with 63 to 107 tons ha−1year−1 of potential biomass production. The integrated process significantly improved the energy balance and economics of biofuel production and also the wastewater treatment plant (WWTP). The economic analysis confirmed that higher biomass production and technology improvement were required to achieve operational feasibility and profitability of the current microalgae-based bio-oil production

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

超小型排水処理を目指した油水分離・浮遊物質回収システムの開発と分離特性の検証

科学研究費助成事業 研究成果報告書：基盤研究(C)2014-2016課題番号 : 2645035