Artificial Intelligence In Agriculture Industry Training Ppt

Slide 1

This slide depicts the agriculture information life cycle and how the implementation of AI can be useful in increasing productivity.

Instructor’s Notes: Agriculture entails processes and phases, the majority of which are performed manually. AI can help with the most complex as well as the routine jobs by supplementing existing technology. When integrated with other technologies, it can gather and evaluate massive data on a digital platform, determine the best course of action, and even initiate that action.

Slide 2

This slide lists advantages of AI in the agriculture sector such as analyzing market demand, managing risks, protecting crops, harvesting, etc.

Instructor’s Notes:

• Analyzing Market Demand: AI can make crop selection easier and assist farmers in determining which product is the most profitable
• Managing risks: Forecasting and predictive analytics can help farmers reduce errors in business processes and lower the chance of crop failure
• Breeding seeds: AI can assist in developing crops that are less prone to disease and are better adaptable to environmental conditions by gathering data on plant growth
• Feeding crops: AI can help determine the best irrigation patterns and fertilizer treatment times and predict the best agronomic product mix
• Monitoring soil health: AI systems may perform chemical soil analyses; based on this, reliable estimations of missing nutrients can be provided
• Protecting crops: AI can track the health of plants in order to detect and even predict diseases, identify and eradicate weeds, and make pest control recommendations
• Harvesting: It is possible to automate harvesting and even forecast the ideal time for it with the use of AI

Slide 3

This slide lists applications of Artificial Intelligence in agriculture which is being used to help produce healthier crops, control pests, monitor soil and growing conditions, organize data for farmers, reduce effort, and greater efficiencies a wide range of agriculture-related operations along the food supply chain.

Slide 4

This slide discusses issues associated with the adoption of Artificial Intelligence in the agriculture sector. Some of these problems are lengthy technology adoption process, lack of technical experience, and privacy & security issues.

Instructor’s Notes:

• Lengthy Technology adoption process: Farmers must realize that Artificial Intelligence is a more advanced version of essential technologies for processing, acquiring, and analyzing field data. For AI to function, technological infrastructure is needed. As a result, even the farms with some technology in place may find it difficult to upgrade
• Lack of Technical Experience: The agricultural industry in emerging countries differs from Western Europe and the United States. Artificial Intelligence in agriculture could assist some locations, but it may be challenging to sell it in areas where agricultural technology is not being popular. Farmers and agriculture business owners ready to adopt innovative solutions will need training and continuing support from digital businesses and their products
• Privacy & Security Issues: Precision agriculture and smart farming creates several legal difficulties that often go unanswered because there are no defined policies and regulations surrounding the use of AI, not just in agriculture but in general. Farmers may encounter significant challenges due to privacy and security threats like cyberattacks and data breaches

Slide 5

This slide talks about the scope of Artificial Intelligence and how it can be combined with other technologies to help agriculture.

Instructor’s Notes:

• Big data for informed decision making: Data analytics in agriculture can result in large productivity gains and significant cost reductions. By merging AI and big data, farmers can acquire credible recommendations based on well-sorted real-time information on crop demands. As a result, guesswork will be eliminated, allowing for more precision in farming methods such as irrigation, fertilization, crop protection, and harvesting
• IoT sensors for capturing and analyzing data: Farmers can monitor, measure, and save data from fields in real-time using IoT sensors and other supporting technology (such as drones, GIS, and other tools). Farmers may acquire more accurate information faster by combining AI agricultural technologies with IoT sensors and software. Better data equals better judgments and less trial and error. On balance, there is net savings of both time and money
• Automation & Robotics for minimizing manual work: One of the most challenging problems in farming is workforce shortage, which can be solved with AI, autonomous tractors, and the Internet of Things. Since these technologies are more accurate and hence eliminate errors, they have the potential to be cost-effective. When combined, AI, autonomous tractors, and the Internet of Things are the key to precision agriculture with less time and money invested on trial and error