- Solution: Rice Pest and Disease Monitoring and Forecasting
- Partner: Chengdu Yunyi Technology Co.
- Seeed Products used: SenseCAP ONE S700 7-in-1 Compact Weather Sensor, Industrial pH Sensor, Dissolved Oxygen Sensor, SensCAP 4G Sensor Hub, SenseCAP Portal and Mobile APP
- Industry: Smart Agriculture
- Solution Deployed in: Guangxi, China
Guangxi is a national rice production region with an annual planting area of over 34 million mu, and is one of the top ten grain-producing provinces in China. During surveys, it was found that pests and diseases are the key factors affecting rice production in Guangxi. The overall occurrence level is categorized as grade 4-5. (which means that without prevention and control, it can cause widespread losses). Each year, an estimated area of around 90 million mu is affected.
The main pests and diseases in the area include stem borer, rice planthopper, leaf folder, rice blast, and rice gall midge. Their occurrence is closely related to the local climate (subtropical climate with moderate sunshine in winter and more in summer, abundant precipitation, prominent droughts and floods), field microclimate (factors such as soil temperature and humidity, light intensity, rainfall, wind), and planting management techniques (fertilizer and pesticide, soil pH, electrical conductivity). Therefore, how to carry out effective prevention and early warning measures has become the key to the battle for food security.
Why Meteorology Matter?
Meteorological data can provide important contributions to the monitoring of rice pests and diseases. The following are some correlations between meteorological data and the monitoring of rice pests and diseases:
- Temperature: The optimal temperature for rice planting is 20°C-35°C. When the temperature reaches a certain range, it is easy to cause outbreaks of certain pests and diseases. By analyzing temperature data, the time and scope of rice disease occurrence can be estimated, and corresponding preventive and control measures can be formulated.
- Humidity: Humidity is one of the important factors affecting the occurrence and spread of rice diseases and pests. For example, rice blast disease is prone to occur in high humidity environments, while the spread speed of rice sheath blight is faster in low humidity environments. By analyzing humidity data, the time and scope of various pests and diseases can be predicted, and corresponding preventive and control measures can be formulated.
- Sunshine duration: Sunshine duration has an important impact on the growth of rice. In conditions of sufficient light, rice grows vigorously, and its resistance to pests and diseases is relatively strong. By monitoring and analyzing sunshine duration, the time of occurrence of pests and diseases can be predicted, and corresponding preventive and control measures can be taken to ensure the healthy growth and harvest of rice.
Rice pests and diseases offen come with diverse types, affect extensive range, and might result in severe outbreak. It is essential to monitor meteorological data effectively so as to take relevant preventive measures at the beginning when pests and diseases are demonstrating the signs of a widespread outbreak, promptly sending out warning to prevent potential disasters.
Traditional monitoring methods will lead to incomplete and untimely collection of information. The speed of rice disease outbreak is very fast, and it takes no more than about 24 hours for pathogens to breed massively and invade the host. The timeliness of early warning is therefore very essential. Without using advanced technology, relying solely on human experience can lead to misjudgment, omission, and delay, resulting in the outbreak and spread of pests and diseases.
Relying on traditional methods and manual control has become inadequate to cope with the constantly changing situation. It is necessary to deploy intelligent monitoring solution to enhance the ability to detect problems in real-time and provide early warning in the field.
Established in 2021, Chengdu Yunyi Technology Co., Ltd. is an IT services and software provider primarily engaged in the field of natural resource protection, smart planting, smart breeding, rural governance, cultural tourism. Integrating big data, AI, cloud computing, blockchain, IoT and other new generation technologies, it aims to build a collaborative innovation platform for government, industry, school, scientific research institutes, and users, promoting technological innovation and digital transformation.
The Rice Pest & Disease Mornitoring and Forecasting solution proposed by Seeed Studio utilize the SenseCAP weather station and sensors to collect real-time environemntal data, soil temperature and humidity, electrical conductivity, and pH values in rice fields. By monitoring these factors, potential rice diseases occurring during the growth period of the rice field can be forecasted in advance, and farmers can be reminded to take corresponding preventive measures accordingly.
Real-time monitoring can guide farmers in developing field management practices, such as irrigation, fertilization, and use of pesticides. The data collected during each stage can be analyzed comprehensively with the meteorological conditions of past pest outbreaks, so as to predict the types, timing, and severity of possible outbreak in advance.
Take the deployment in Guangxi as an example:
- Several monitoring points have been set up, the data collected by which can represent the meteorological data within a range of 1-2 km.
- The equipment is waterproof, sun-proof, and has strong expandability. It is easy to deploy and supports offline data caching.
- The management platform can monitor real-time data on rice growth, climate, and soil. The data collected by the platform devices can be traced back and visualized on the website/mobile app, which can achieve advance prediction and disaster classification. Combined with the rice growth cycle, it guides farmers in field management, reminding them to take appropriate prevention measures in a timely manner to improve production efficiency.
- By integrating historical meteorological data on the occurrence of diseases and pests, the platform provides alerts on potential disease types, timing, and severity based on current weather conditions. It prompts plant protection personnel to conduct inspections and take necessary actions.
- Agricultural experts can provide remote guidance on prevention and control programs.
In the future, this system can be continuously upgraded. For example, by using edge computing, plant disease automatic identification can be achieved through field cameras or smart agricultural drones for inspection. Devices like smart agricultural drones can simultaneously conduct disease area mapping and pesticide spraying path planning.
This project also contributes to the following Sustainable Development Goals (SDG 2, 9, 11, 13, 15, and 17).