Pineapples that are born from technology is resulted from long-term contributions by different experts from many domain.。
- specification ration from 50% to 80%
- Translucency ratio from 8% to 1%
Using field data collection and data analysis techniques in the field, we conduct long-term monitoring on pineapple fields and collect data of environmental variables. By means of big data statistical analysis, models that help analyze and identify important environmental variables related to crop production quality are established.
- The focus of agricultural enterprise cultivation management
- Predict yield and quality
- Stabilize the supply of high-quality products
- Stabilize the income of agricultural enterprises
- Provide early warning and production risk management
Data prediction planning
Through the field experimental design, the collection of environmental parameters in the field, and the field records and field surveys of agricultural enterprises, we can actually understand the current situation of the production area and the market demand of agricultural products. At the same time, through research collection and expert discussions, we systematically collect crop yield and quality data to establish a growing environment and crop quality database.
Set up predicting model
Through the establishment of database, data fusion and cleaning are further carried out ,with combining agricultural knowledge, designing effective parameters, and constructing calculation methods such as machine learning, deep learning, data exploration, etc., analytical predicting models for environment and crop quality factors can be developed.