Linguistic resources such as ontologies or thesauri play a key role in the effective utilization of electronic data. To reduce the time and effort required to develop such resources, automated systems are used to acquire linguistic information. However, this type of information tends to be fragmented, discontinuous, and incomplete. We extracted agricultural linguistic information from corpora by using pattern-based automatic acquisition of related hyponyms and automatic acquisition of similar expressions based on word dependency. We then combined this automatically extracted information with AGROVOC, an existing linguistic resource on agriculture, forestry, and related topics. We were able to connect much of the extracted information to AGROVOC. Fragmented and incomplete information could be supplemented as a part of the AGROVOC system, and new terms were included and existing terms were expanded.

View full abstract

The intent of this study was to develop a technique for weight and ripeness estimation of oil palm (Elaeis guieensis Jacq. var. tenera) bunches from hyperspectral and RGB color images. In the experiments, color and hyperspectral images of the bunch were acquired from four different angles, each differing by 90 degrees. Acquired RGB color images were converted to HSI and L*a*b color space. Gray-scale thresholds were used to identify the area of the bunch and the area of space between the fruits. The total number of pixels in the bunch and the space were counted, respectively. In the hyperspectral images, the total number of pixels in the bunch was also counted from an image composed of three wavelengths (560 nm, 680 nm, and 740 nm), while the total number of pixels of space between fruits was obtained at a wavelength of 910 nm. From these sets of data, weight-estimation equations were determined by linear regression (LR) or multiple linear regression (MLR). As a result, the coefficient of determination (R²) of actual weight and estimated weight were at a level of 0.989 and 0.992 for color and hyperspectral images, respectively. Estimation of oil palm bunch ripeness was also tested. Bunches belonging to 4 classes of ripeness (overripe, ripe, underripe, and unripe) were used for this study. Since ripeness estimation from overall data from a bunch was quite difficult, we focused on the difference in colors or reflectivity of the portion concealed and not-concealed with fronds. Euclidean distances between the test sample and the standard 4 classes of ripeness were calculated, and the test sample was classified into the ripeness class that had the shortest distance from the sample. In the classification based on color image, average RGB values of concealed and not-concealed areas were used, while in hyperspectral images the average intensity values of fruits pixels from the concealed area were used. The results of validation experiments with the developed estimation methods indicated acceptable estimation accuracy, and a possibility for practical use to estimate the ripeness of oil palm bunches.

View full abstract

We integrated a search and map display system for the collection site(s) of selected plant accessions with climate, morphological, and growth evaluation databases. This integrated system uses a combined query system that can be used on the Internet and allows a user to see passport information, evaluation data, and seed morphology of selected accessions on a distribution map. The system can also display the collection sites of accessions using user-provided data. A mathematical (statistical) index to estimate geographical clines and specificity of some plant traits is also provided. Accession locations can be plotted with their associated molecular information on the map using information provided by the Genebank Web page. System users are able to see a visual presentation of the location and surrounding environment of selected plant accessions. As a result, scientists can estimate suitable growth conditions of the accessions and make plans for the further strategic germplasm collection. This system is expected to contribute to a greater understanding of plant genetic resources, as well as their use and conservation.

View full abstract

In the arid areas of Northwest China, agriculture especially crop farming consumes most of water resource. Water-intensive and low value-added wheat is cultivated as a staple food of the local people in large scale. Because of the surface water scarcity, irrigation mainly depends on pumping the groundwater in these areas. Based on field surveys conducted in 2005 and 2006 both in the south and the north of Minqin County, Gansu province, this study is designed to study the role of groundwater irrigation on wheat production and how to improve the technical efficiencies (TEs) of wheat farmers by estimating a Stochastic Frontier Production Function (SFPF). The above information is valuable for how to save wheat water consumption and improving the food safety in the arid areas. The empirical results showed that expenditure on pumping groundwater played important role in wheat production. Meanwhile, the same expenditure played more effectively on wheat intercropping with maize in the south than that in the north. As a result, the farmers in the south are more likely to increase expenditures on pumping water than the farmers in the north. Therefore, the government should make new program to restrict wheat water consumption and to secure the farmers in both areas to use the groundwater fairly. Meanwhile, it is suggested that younger farmers can achieve a higher TE both in the south and the north. For the input-intensive intercropping in the south, a larger family, higher education of the farm managers, and coordination of their part-time jobs with agricultural production activities can also help to get higher TEs.

View full abstract

Google Maps and Google Earth have improved the functions and convenience of Web map services in recent years. Existing data are often related with geographical points, and many Web applications (mashups) that connect maps and existing data have been developed. Agricultural models often deal with data related to geographical points, and a map interface is often used to select a geographical point and display data in a plane. Before Google Maps was introduced, we developed agricultural models with a map interface using ChizuBroker. For the present study, we created a new map interface using Google Maps and compared the functionality of the Google Map interface with the ChizuBroker interface. We also demonstrated the use of Google Maps and Google Earth for an agricultural model and data integration system by showing time-series data and the superposition of data on a map.

View full abstract