China Pilots 2 and 3
ISSCAS
The CHina Pilots 2 and 3 are managed by ISSCAS. China pilot 1 is affiliated to the Bei'an Administration of Heilongjiang Land Reclamation Bureau and located in Wudalianchi City and Bei'an City, Heilongjiang province, China. China pilot 2 is located in Liuhe district, Nanjing city, Jiangshu Province, China.
PILOT CHALLENGES (CONTEXT)
The CHINA Pilot 2 - low level of awareness, lag in variety, over-application of chemical fertilizers. The CHINA Pilot 3 - Small-scale farming, nutrient imbalances, over-application of chemical fertilizers, soil contamination.
PILOT OBJECTIVES
China pilot 2 and 3 objectives are as follows:
To improve the farmland information level
To realize crop growth automatic monitoring
To realize the intelligent process and visualization of field data
To use deep learning algorithms to optimize the input dosage and timing of water, fertilizer, medicine, and seed, to achieve optimal growth of crops, increase crop production potential, and reduce the use of chemicals
PILOT INNOVATIONS
The pilot innovations mainly consist of advanced strategies and technologies, soil quality improvement, and a soil management intelligent service platform that can be customized for multiple terminals.
table
Pilot location | China |
Use Case | CHINA Pilot 2: the Bei'an Administration of Heilongjiang Land Reclamation Bureau and located in Wudalianchi City and Bei'an City, Heilongjiang province, China CHINA Pilot 3: Liuhe district, Nanjing city, Jiangshu Province, China |
Pilot scale | CHINA Pilot 2: The area is 42 hectares. CHINA Pilot 3: The area is 3.2 hectares. |
Land uses (crop types) | CHINA Pilot 2: Major crops are corn and rice with single cultivation. CHINA Pilot 3: The typical cropping system is a rotation between rice and winter wheat. |
Major agricultural and environmental projects | Promote the trial work of farmland rotation and fallow. |
Pilot objectives | Improve the farmland information level, realize crop growth automatic monitoring, and realize the intelligent process and visualization of field data. Using deep learning algorithms to optimize the input dosage and timing of water, fertilizer, medicine, and seed, to achieve optimal growth of crops, increase crop production potential, and reduce the use of chemicals. |