AI Use Cases
Specify approach to crop growth based on individual plot characteristics and real time data
Specify crop growth techniques based on individual plot characteristics and relevant real time data. Scaling out best practice in agriculture will be potentially transformative when applied to the world's nutrition challenges.
Predict risk of animal health issues through analysing audio patterns
Predicting risk of health issues can be complex - especially when the creatures in question are not mammals. One example solution is using an app to listen out for the health of a colony of bees. The system has been trained to recognise sounds indicating stress or disease that may have afflicted a beehive.
Identify and validate a molecule to target with a drug compound for agricultural use
During the initial phase of drug research and development, the target for a new drug treatment must first be chosen. The target molecule will be what the drug compound interacts with to get the intended outcome, often the treatment of a disease. Researchers use deep neural networks to predict molecular level interactions to treat a condition or improve particular functionality
Predict and optimise pricing based on future market, weather, crop yield and other forecasts
Optimise pricing (ideally in real time) based on factors such as future market, weather, and predicted crop yields. Clarity on pricing can be of especial assistance to those at the production end of the market chain.
Optimise crop or livestock yield management based on field sensors
Yield management by taking sensor data on soil quality - common in newer John Deere et al truck models and determining what seed varieties, seed spacing to use etc. Also used for checking the right moment to bring livestock to market - using cameras for example.
Optimise greenhouse climate environment to maximise production output
Environmental control system for automated greenhouse plant habitats is trained on a dynamic climate model. This then enables it to optimise inputs of energy, moisture and nutrients to maximise plant growth productivity. The need to optimise space utilisation for urban farming helps justify the investment required.
Test soil and water samples with handheld device
Mobile testing of soil and water for chemicals radically speeds up the process and can enable local decisions on critical matters in a time-sensitive fashion. There will be a library of chemical indicators that expands over time. The application might work in conjunction with a physical testing device or product and the AI will interpret the signalled outcome.
Optimise agricultural production process often in real time
Optimise farming production process, often in real time, determining where to dedicate resources to reduce bottlenecks, cycle time and error rates. Interventions might range from automated fertiliser or pesticide deployment to alerting for human involvment.
Tracking, monitoring and analysing livestock behaviour to optimise production
Rather like humans animal health and welfare is strongly related to the exercise and diet that they experience. Hence using data generated by wearables (attachables...) and other sensors to predict animal outcomes should help produce better farming yields (in both quantity and quality terms).
Confirm animal identity through eye scans
HIgh value animals can need identifying at critical moments - for example to ensure that the right horse has been entered in a race or has been provided to stud. Individual eye scans are kept on record and then a portable scanner can be used to ensure that the right aninal is present.
Optimise blend and process timing for raw material inputs to refining and similar processes
Mining And Metals
Optimise blend mixture and process timing of raw materials being used in refining and similar processes. Sustained small marginal improvements can have a significant cost impact - especially where human involvement can be costly or potentially hazardous.