Challenges and Considerations in Implementing AI in the Poultry Industry

• Artificial Intelligence (AI) is becoming crucial for different industries which aim to improve operational efficiency, generate revenue, and other benefits.
• The state of AI in 2020, a McKinsey global survey, reported that 50% of respondents said “their companies have adopted AI in at least one business function.” The numbers are projected to keep increasing in the coming years, and the revenue AI will generate will double.
• AI development and implementation challenges include:
  • Determining the right data set while having a trusted source of relevant data that are clean, accessible, well-governed, and secured.
  • Eliminating bias problems by either training AI systems with unbiased data or developing easily-explained algorithms that can be easily read.
  • Managing data security and data storage issues concerning sensitive data and training algorithms for AI applications.
  • Providing suitable infrastructure and premium processing capabilities that are secure and compatible with existing systems.
  • Integrating AI into existing systems with the support of AI solution providers with extensive experience and expertise. After transition, employees need appropriate training on working with the new system.
  • Achieving the computing power for processing the vast volumes of data required for building AI systems which can be challenging, especially for startups and small-budget companies.
  • Finding people with the right skillset and expertise for AI implementation and deployment.
  • Managing expenses, as AI specialists can be expensive and currently quite rare in the IT market.
  • Addressing legal concerns around AI development and implementation.
  • Solving explainability in AI by providing transparency in AI decisions, inspecting the impact of artificial intelligence on decision making, providing frequent audits of their systems, and having regular training.
• Despite the challenges, upon adopting AI in a business, profits improve by up to 30%, process efficiency increases by up to 50%, decision-making improves by up to 50%, and customer satisfaction increases by up to 70%.
• The poultry industry could benefit from AI implementation for tasks, including the following:
  • Monitoring the flocks’ weight and size individually and collectively over time to identify any deviations, which can be revealing early symptoms of health conditions or poor performance.
  • AI can efficiently predict the weather, water level, and other environmental factors that immediately affect the bird’s health.
  • AI applications are being developed for detecting and monitoring bird health, including sensors to determine animal activity levels, water and feed intake levels, and automatically detect or predict diseases.
• AI in the poultry industry can help decrease bird mortality, increase flock

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• Agricultural robots offer numerous benefits in farming production. They possess advanced perception, autonomous decision-making, and precise execution abilities.
• Agriculture robots can be classified based on their application scenarios (fields, orchards, farms, etc.) and their position in the industrial chain (seeding, planting, nurturing, harvesting, and processing).
• Tillage robots refer to intelligent machines that are utilised to cultivate the land. Tamaki et al. developed a robotic system with three robots for large-scale paddy farming, featuring a tillage robot navigated by RTK-GNSS and IMU or GPS compass.
• Seed-sowing robots are designed to sow seeds in exact positions, saving time and cost. Many functional seeding robots have been invented and put into extensive practice.
• Field robots include autonomous, decision-making, mechatronic, and mobile operation devices that can accomplish various crop production tasks semi-automatically or automatically. Caterpillar and drone-based field robots are rare.
• Multisensor collaborations, advanced visual image processing technology, sophisticated algorithms, and flexible locomotion control are usually indispensable in constituting an agricultural robot.
• Animal husbandry robots include robots used in poultry (as mentioned in the introduction), which perform tasks such as egg collection.
• The article also highlights several noteworthy advancements in agriculture robots such as navigation algorithms based on perception, UAVs designed for agriculture, a high-precision control system for field robots, and a flexible end effector to pick tomatoes.
• However, the article does not discuss the challenges and considerations of implementing AI in the poultry industry.

• Reddit post about AI applied to farming and agriculture
• AI could benefit agriculture the most (yield prediction)
• Free app that would allow anyone to build AI models without writing code
• User is a software engineer in big tech (specialized in AI / machine learning)
• Looking for feedback on what problems farmers are encountering and what problems AI could solve
• Specifically mentions picking the right fertilizers and optimizing crop placement
• Asks if farmers collect data in order to solve problems
• Reddit user would buy a variable rate optimum nitrogen prescription that can be proven via trial data
• Other Reddit user mentions weather as an issue
• Inconsistency in soil sampling tests across labs and spatial differences
• Imprecise ways of fertilizer application can result in too much or too little application
• Compac Spectrim vision system based around AI for produce sorting and grading
• Exeter Engineering’s AI grading feature
• Precision Farming as a form of AI that uses soil and yield data to adjust the application of crop inputs
• Some farms soil test every year while others sample in smaller grids
• In-season crop monitoring, crop genetics and variety improvements, and autonomous farm equipment as areas in need of improvement according to a Reddit user

• AI (Artificial Intelligence) has the potential to change the way businesses operate completely.
• While AI can improve efficiency and increase revenue, there are challenges in its implementation.
• The top 5 challenges in AI implementation are:
  • Data Quality and Availability
  • Cost of Implementation
  • Integration with Existing Systems
  • Lack of Skilled Professionals
  • Ethics
• Data Quality and Availability:
  • The quality of data is crucial for AI. Data needs to be accurate, reliable, and relevant. Without proper data, AI algorithms can’t learn and improve.
  • Not all companies have access to relevant data, or in some cases, data quality may not be good enough for AI implementation.
  • Possible solutions include investing in data collection and management, collaborating with other companies to access necessary data, or generating synthetic data.
• Cost of Implementation:
  • The implementation of AI can be costly, and many companies may not have the budget to invest in it.
  • Possible solutions include outsourcing to AI service providers, investing in resources to develop in-house AI expertise, or gradually implementing AI through small projects.
• Integration with Existing Systems:
  • Implementing AI can be challenging due to integration issues with existing systems.
  • Possible solutions include developing APIs for systems to communicate with each other, identifying the most suitable systems to integrate, and conducting a pilot test before full-scale implementation.
• Lack of Skilled Professionals:
  • AI implementation requires skilled professionals such as data scientists, machine learning experts, and AI engineers. There is currently a shortage of such professionals.
  • Possible solutions include investing in training and education programs for existing employees, partnering with universities or colleges, or outsourcing to AI service providers.
• Ethics:
  • AI raises ethical concerns, such as bias, transparency, privacy, and job displacement.
  • Possible solutions include developing ethical guidelines and frameworks, involving stakeholders in the development process, and promoting transparency and reporting.
• Companies should assess their readiness for AI implementation by considering their business goals, capabilities, and resources.
• Companies should also develop a clear AI strategy, set goals, and measure success based on KPIs (Key Performance Indicators).
• Successful AI implementation requires continuous monitoring, testing, and improvement.
• AI implementation should focus on process optimization, enhancing customer experience, and creating new products and services.

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💭  Looking into

The article should describe successful use cases and benefits of AI in the poultry industry, such as predicting animal health, optimizing feeding efficiency, and reducing waste.

💭  Looking into

The article should explain the main challenges and limitations of implementing AI in the poultry industry, such as ethical concerns, high costs, and limited accessibility for small-scale farmers.