We have been developing low-cost environmental sensors for general farm use. As the use of wireless networks has grown, the need for equally low-cost independent power supplies has risen. Although the price of solar panels has dropped greatly in recent years, the price of independent power supplies has not. It is necessary to provide a cheap, independent power supply in order to make its combination with low-cost environmental sensors accessible to the general market. Here, we examined several combinations of solar panels with storage batteries under various electrical loads to determine the most cost-efficient one for use with environmental sensors. We designed low-cost, independent power supplies both to run environmental sensors and as gateways for data collection. We were able to reduce the cost of the power supplies by optimizing the load current at the sensor. We were also able to reduce the total cost of an environmental sensor system by using a solar panel to measure light as well as to supply energy. Experiments in the south of Tokushima prefecture demonstrated the electrical stability of these independent power supplies.

Soil matric potential (ψ_m) is an important property, and its measurement is necessary to determine the availability of soil water for plants. The objectives of this study are to develop a low-cost ψ_m sensor using porous media in conjunction with a dual probe heat pulse (DPHP) technique and to evaluate the performance of the developed sensor. The DPHP ψ_m sensor measures the volumetric heat capacity and thermal conductivity of the porous media and converts these thermal properties into a ψ_m value. The performance was evaluated by comparing the output of the developed DPHP ψ_m sensor with that of a commercialized ψ_m sensor, and the temperature dependence of the developed sensor was also investigated. The volumetric heat capacity and thermal conductivity of the porous media measured with the DPHP method were converted to their corresponding logarithmic value of ψ_m by means of two intersecting linear functions. The use of thermal conductivity to determine ψ_m was found to yield more accurate results than the use of volumetric heat capacity, and the accuracy of the determined ψ_m value was approximately 10%. The DPHP ψ_m sensor was found to be less sensitive to temperature than existing commercialized ψ_m sensors. Furthermore, the volumetric heat capacity showed slight temperature dependence regardless of soil water content. The developed DPHP ψ_m sensor was found to be versatile because it can use either volumetric heat capacity or thermal conductivity, depending on the magnitude of soil temperature variation and required accuracy. The DPHP ψ_m sensor is intended to contribute to expanding the use of information and communications technology (ICT) in agriculture as a low-cost ψ_m sensor.

Much information is now stored “in the cloud”, but time and labor to input information pose a bottleneck. Here, we propose a system for gathering environmental data automatically and estimating the work needed from the acquired data with the aim of reducing labor through the automation of farm work records. Our method obtains information using radio frequency identification in the UHF band and categorizes farm work using a naive Bayes classifier. To disseminate this system, we developed it with a low cost structure. In this paper we report the configuration of the proposed system and the results of a demonstration.

We describe a method for automatically extracting fruit growth information from multiple apple tree images using a high-resolution image monitoring system. First, Green-Blue Vegetation Index images were created from the green and blue channels in 3,281 high-resolution RGB images taken from April to December 2016. Leaf zones were extracted from the background, and their area and fractal dimension were calculated. Their statistical properties were evaluated, data were sorted, and fruit growth was approximated. Radius (in pixels) was used as an index of fruit growth, and 1,006 measurements were made from 6 fruits in a Web image viewer. When the explanatory variable was the integrated value of the area or the fractal dimension of the leaf area and the objective variable was the daily average value of the fruit radius, the coefficient of determination by logistic curve approximation was ≥0.99. These results suggest that fruit growth information can be extracted automatically from a large number of high-definition images.

Efficient breeding of new plant cultivars is necessary to maintain regional agricultural production under global warming. New Breeding Technology (NBT) has been developed to improve the accuracy and efficiency of plant breeding. To assess the likelihood of diffusion of NBT, we examined consumers’ attitudes toward apples developed using flowering promotion technology. Consumers’ preference is considered to be different from that of the conventional genetically modified crops because the genetic recombinant flowering technology is used only during breeding process, and the resultant fruits do not contain foreign genes. The results of a double-bounded, dichotomous choice, contingent valuation method (CVM) indicated that consumers who did not perceive a difference from regular apples tend to have a willingness to pay for those developed using the flowering promotion technology. Explaining the necessity and importance of this technology under climate change conditions is also effective at enhancing the likelihood of diffusion.

This research evaluated the benefit of utilization of biofuel for internal combustion engine (ICE) vehicles and electric vehicles (EVs) in terms of environmental benefit over GHG emission reduction in Indonesia and Japan. To support the research, the forecasted GHG emission from these mobility alternatives (rather than fossil fuel internal combustion engine) within the time frame between 2015 and 2030 was reported and analysed. The life cycle assessment (LCA) of palm oil-based biodiesel production, ICE vehicles, EVs and lithium battery production were analysed by using SIMAPRO^®. The results showed that Japan has a better emission reduction strategy than Indonesia. Lower emission factor from power sector provides more advantage to promote EVs in Japan and expected better than other alternatives in current situation and future. On the other hand, for Indonesia, both biofuel-ICE and EVs utilization could not offer high positive impact to promote green mobility. Biofuel is insufficient to achieve emission reduction in Indonesia for transportation sector, only less than 8.0% target for transportation in 2020 and worsening of emission factor from Indonesia power generation for current situation and future. Emission mitigation strategy of the road transportation in Indonesia requisites a clearer direction, especially for future targeted biofuel crops and cleaner electricity sources.

To use farm machinery efficiently, it is necessary to optimize methods to the conditions on each farm. To plan optimal machinery use, it is necessary to record machine operating data in the field and analyze the working area, work content, and working hours. These results can then be used to devise the optimal working method. We used an inexpensive machine-mounted commercial GPS receiver to analyze the trajectories of the harvester and the transporter in order to calculate the actual working hours with a low margin of error. The results showed effective working efficiencies of 0.29 for unidirectional harvesting and 0.37 for bidirectional harvesting by a small harvester, and 0.27 and 0.38, respectively, by a medium-sized harvester. The effective and calculated field capacities of the medium-sized harvester were nearly twice those of the small harvester. Analysis of the simultaneous movement of the harvester and transporter can identify inefficient work areas and possibly improve the working efficiency. The small harvester has the potential to raise the working efficiency by optimizing cooperation with the transporter. Considering the influence of precipitation on machine travel, we found that the operational rate of the small harvesters was 6% higher than that of the medium-sized harvester.

We developed software to calculate the greenhouse interstice ventilation rate based on the nocturnal time course of inside and outside CO₂ concentrations. The software runs on a Windows computer, but it is necessary to install a sensor node capable of measuring CO₂ in conformity with the Ubiquitous Environment Control System standard. In the winter, the CO₂ concentration usually increases at night in a closed greenhouse because CO₂ is generated by the respiration of soil microorganisms and the crop. The time course of CO₂ is used to define the frequency of ventilation of the greenhouse and the rate of CO₂ generation. After the control period between the start and stop times and parameters of greenhouse size for estimating the ventilation rate and the rate of CO₂ generation are defined, the software records CO₂ concentrations during the control period. When the stop time is reached, the software automatically calculates the ventilation frequency and the rate of CO₂ generation by non-linear regression analysis. It can also use the calculated values to calculate the crop photosynthesis rate. The software’s outputs can be saved as text and graphics files and can be sent via e-mail to allow remote monitoring. The data can be used to improve environmental conditions by managing temperatures and greenhouse CO₂ concentrations and can thus reduce energy consumption through improved ventilation management.