Gluten-free rice flour is used in food processing. The quality of the product during storage may be influenced by intrinsic properties and environmental factors such as temperature, humidity, and oxygen exposure. Indica rice, mainly grown in the tropics, is long, slender, and high in amylose than other varieties. In this study, we investigated the changes in the physicochemical properties of Indica rice flour under varying hermetic conditions; ambient, vacuum, CO₂-filled, and bagged during storage at 30°C for 3 months, aiming to identify an economically viable storage approach for tropical regions. The moisture content decreased over the observed period. The apparent amylose content remained relatively unchanged. The water absorption index, water solubility index, and swelling power index exhibited a rapid increase in the initial month and then steadied with slight upward fluctuations. Catalase activity and pH, showed that stored flour deteriorated gradually over time. The storage conditions significantly affected the moisture content and pH values but storage time had a greater influence on all measured parameters. The ambient storage condition effectively minimized moisture loss and pH decline over three months, offering a cost-effective storage solution ideal for low-income farmers in the tropics.

This study evaluated the characteristics of 3D point cloud generation for estimating leaf area in floricultural crops using a smartphone-based scanning method. Focusing on stock (Matthiola incana (L.) R. Br.), a key winter-season flower cultivated in Namie Town, Fukushima Prefecture, we investigated the influence of scanning conditions, specifically the distance between the camera and the plant, and the number of scanning laps, on the accuracy of leaf area estimation. An iPhone 12 Pro with the Scaniverse application was used to acquire 3D data of a single stock plant grown under controlled greenhouse conditions. Multiple 3D models were generated by varying the camera-to-subject distance (5-40 cm) and scanning laps (1-7), and these were compared against a reference model obtained through extensive scanning. To evaluate model quality, the generated point clouds were registered to the reference model. Individual leaves were segmented, meshed, and their surface areas calculated. Absolute Percentage Error (APE) and Mean APE (MAPE) were used as accuracy metrics for individual and total leaf area estimation, respectively. Results showed that increasing the scanning distance (up to 40 cm) and using a moderate number of laps (up to 5) reduced missing parts on point clouds and improved leaf area estimation accuracy. Although shorter distances yielded denser point clouds overall, the narrower field of view resulted in incomplete capture of outer leaves, leading to reduced accuracy. These findings highlight the importance of optimizing scanning distance and lap number to minimize point cloud loss and enhance estimation performance. This approach offers a practical and non-destructive solution for plant phenotyping using widely accessible smartphone devices.

Promoting tourism resources are important issue for local governments. In this study, Akima Bairin, or Akima plum grove, which is famous for its plum blossoms, in Annaka City, Gunma Prefecture, was selected as a study site. We created a 53-second tourism PR video including 3D models using aerial photos and videos taken by a small drone, which have become increasingly popular in recent years. 3D reconstruction software was used to build 3D models and also create animations. Then, we investigated the advantages of this method. Aerial video captured by a small drone enabled a dynamic expression which was different from the viewpoint from the ground. Also, by using 3D models, the viewpoint movement which was not actually recorded in situ can be simulated. The methods presented in this study can be used to create PR videos promoting local natural tourism resources.