AI4EO in Agriculture
Diving into the applications of AI combined with EO in agriculture , it is assumed that IOT/in situ sensors such as soil sensors ,weather sensors and satellite imagery are integrated to provide a comprehensive view of the ecosystem. In some cases fusion of aerial imagery from UAE/drones are also considered. Satellite imagery can be obtained by using various type of sensors such as multispectral, hyperspectral, Synthetic aperture radar (SAR) and thermal. In some cases more than one kind of imagery might be used to obtain a well-rounded perspective of the area of interest.
Below are a dozen use cases that can be achieved by AI4EO in agriculture,
Soil monitoring which mainly gives information about the soil moisture , soil temperature and salinity level which facilitates the process of crop rotation.
Crop monitoring which gives information about the crop yield, nutrient composition of crops and diseases in crops.
Real time monitoring of weather parameters such as air pressure, temperature , wind speed and humidity which allows the appropriate application of water by supporting the concept of smart irrigation.
Real time monitoring of the photosynthesis process parameters such as plant temperature, plant moisture , amount of Chlorophyll etc can be achieved which determines the performance of the crop.
Predictive analysis of crop yield , diseases in crops, presence of pests, weeds, aids in the efficient use of fertilizer, pesticide and water. Plant identification is also possible via AI which would is essential in differentiating between weeds and plants.
Predictive analysis also helps identify weather patterns and market trends, which in turn helps crop management.
AI powered autonomous systems can be used in the field to navigate across the field in an efficient manner and spray pesticides, fertilizer, water etc
AI algorithms aid in precision planting by analyzing historical data, weather conditions, and soil health. This ensures that crops are planted in the most favorable locations and at the right times.
Optimization of the agro-supply chain from prediction to distribution by predicting harvest times, managing warehousing, and optimizing transportation routes for maximum efficiency.
AI assisted market trend analysis aids the farmer in crop selection and pricing
AI can assess risk factors such as weather conditions, disease prevalence, and market fluctuations to provide accurate risk assessments for crop insurance purposes. This helps farmers manage financial risks associated with uncertainties in agriculture.
From an environmental perspective drought monitoring, carbon sequestration monitoring , energy utilization , biodiversity monitoring , vegetation count in order to predict food security is possible.
Above specified are just a handful of applications for AI4EO, however there are still numerous use cases for the same which are waiting to be unlocked across different geographies and different kinds of farms and forests.