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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Water usage optimisation

Introduction

The growing global population has increased the demand for food, emphasizing the need for effective water management in agriculture. Agriculture accounts for approximately 70% of global freshwater withdrawals, facing challenges such as water scarcity, pollution, inefficiency, and climate change. However, advancements in Machine Learning (ML) and Artificial Intelligence (AI) offer promising solutions, including precision irrigation, real-time monitoring, and water-efficient crop management. These technologies analyze vast datasets to optimize irrigation schedules, predict weather patterns, and recommend suitable crops, ensuring efficient water use, healthier soils, and higher yields while reducing waste. Despite their potential, integrating AI and ML into agriculture faces hurdles like high implementation costs, data privacy concerns, skill gaps, and regulatory challenges. Small-scale trials have shown promise, but scaling up these technologies requires innovation and policy support. With continued development, AI and ML can transform water management in agriculture, enabling farmers to make data-driven decisions, conserve resources, and meet the growing demand for food sustainably amidst climate uncertainties.

Challenges

There are several water management challenges in the agriculture sector. The first one is water scarcity, which is compounded by climate change, increasing population, and industrialization. This is especially a challenge in the dry lands since water is a sensitive and a scarce resource. Second is the issue of water pollution as a result of application of fertilizer and pesticides in large quantities that leach into water sources threatening the health of man and other organisms. Thirdly, there is the issue of wastage of water especially due to the use of outdated methods of irrigation and absence of proper knowledge on soil moisture and weather conditions. Fourthly, farmers do not have access to efficient predictive tools that enable them to foresee changes in weather conditions or even pest incidences which results to unnecessary use of water and chemicals.

AI Solutions

These challenges are being addressed by the AI and ML solutions as they gain popularity in the farming sector. Higher level of data analysis can assist in the prediction of weather as well as the condition of the soil which makes it easier for farmers to schedule their irrigation time. Water requirements and pests and diseases incidence can also be determined by AI algorithms to enable proper use of water and pesticides. The analysis of the data from the satellite images with the help of ML can give real-time information about the growth of crops, moisture in the soil and evaporation rates. This can be followed by the use of IoT devices which can help in the automation of the irrigation systems with water being used only when and where it is required. Some of the examples include Aerobotics, Prospera, and Farmlogs that are applying these technologies to offer farmers recommendations on water management. Another example is IBM’s Watson Decision Platform for Agriculture which uses AI and weather information to give farmers water use recommendations.

Benefits

There are a number of ways in which AI can be used in water management in agriculture and each of them presents a number of advantages. Firstly, it can cut down on water consumption thus being a vital tool in the fight against water shortages and the conservation of water resources. Secondly, it is capable of improving the output of crops and their quality through efficient watering. Thirdly, it may also eliminate the need for a farmer to stand guard over his crops and set irrigation schedules automatically, thereby saving on time and resources. Fourthly, it can assist in managing the impact of climate change by allowing the farmers to alter their water management according to the current weather conditions. Lastly, through minimizing the use of chemical fertilizers and pesticides, AI can also help in preserving the health of other organisms and does not pollute the water sources.

Return on Investment

The return on investment (ROI) of using AI in water management in agriculture can be significant. While the initial investment in AI technologies may be high, the long-term savings in water and other resources can more than offset these costs. For instance, a study by Ceres Imaging, an agricultural AI firm, showed that almond farmers in California could save up to $500 per acre annually by using AI-driven irrigation strategies. Moreover, increased crop yields can generate additional revenue for farmers. The ROI can also be seen in terms of environmental benefits, such as reduced water pollution and improved resilience to climate change.

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