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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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Identification of crop diseases

Introduction

The agricultural sector has been one of the most important parts of society for a very long time and is still relevant in the modern world. Artificial Intelligence (AI) has emerged as one of the most valuable technologies in the present world and one of its applications in agriculture is Computer Vision (CV) AI. CV AI is a type of artificial intelligence in which computers are trained to process and understand visual information. On the agricultural sector, it is being employed in the detection and prevention of crop diseases which are among the most devastating factors that affect crop productivity.

Challenges

There are many challenges that affect the agricultural industry which CV AI aims to solve. One of the major problems is the identification of plant diseases at the initial level. Conventional approaches to disease identification include the use of farmers or agricultural experts who have to look out for the symptoms of the disease by direct observation. This makes the process very subjective and also rather slow, with the results being rather arbitrary. There is also a shortage of personnel in the field of plant pathology where the demand for skilled personnel is ever rising. Another challenge is the challenge of the right treatment at the right time after identifying the disease. The process is also hindered by issues such as environmental conditions such as changes in climate that may affect the development of diseases and their spread.

AI Solutions

CV AI provides a solution to these challenges through various methods. Firstly, it allows for automated, real-time monitoring of crops through the use of drones or fixed cameras. These devices can capture images of the crops, which are then analyzed by AI algorithms for signs of disease. This can significantly speed up the disease identification process, allowing for quicker intervention. Secondly, CV AI can also assist in the application of treatment. For instance, precision agriculture techniques can use AI to determine the exact amount of pesticide needed, reducing waste and environmental impact. Lastly, AI can help predict future disease outbreaks by analyzing historical data and current environmental conditions.

Benefits

There are numerous advantages of using CV AI in agriculture. It is thus effective in preventing huge losses that are occasioned by diseases by detecting them in the earliest stages. It also minimizes the need of employing human workers for disease detection thus balancing the work force. In addition, the precision agriculture strategies that are made possible by AI can result in better resource management thus cutting down on costs and impacts on the environment. All in all, CV AI is the perfect tool to enhance agriculture and make it more effective while at the same time being eco-friendly.

Return on Investment

The ROI of CV AI in agriculture can be quite impressive. According to a study by Accenture, AI has the potential of contributing $5.87 trillion to the global GDP by 2035 and agriculture is one of the sectors that will be most impacted. The losses that can be avoided on crops and the improvements that can be made on resource utilization alone can be very valuable. In addition, the application of AI can also create new markets for farmers as the demand for produce grown using technological advancements such as AI increases from the consumers. Still, the ROI will vary depending on the application of AI and the situation that it is applied to.

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