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How To Nail Your Next Tech Interview

AI in Agriculture: Precision Farming and Crop Management
Hosted By
Ryan Valles
Founder, Interview Kickstart
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AI in Agriculture: Precision Farming and Crop Management

The world's agricultural systems are struggling to meet the growing demand for food supply in the face of previously unheard-of issues brought on by population growth. But with environmental issues like deforestation, soil erosion, global warming, and diminishing water supplies, ensuring food security is getting harder. In this scenario, agriculture and artificial intelligence (AI) together have the ability to completely transform farming practices and spark a second green revolution.

Here’s what we’ll cover:

  • Artificial Intelligence and Machine Learning
  • AI and Object Detection
  • AI and Disease Detection
  • AI and Pest Detection
  • AI in Precision Agriculture: The Future
  • AI in Agriculture Crop Management
  • Technologies that Help Maximize Farm Yield
  • The Future of Agriculture will be Technology-Empowered
  • FAQs about AI in Agriculture

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning are being driven by modern agriculture. Machine learning is a subfield of artificial intelligence that enables computers to identify patterns in data, learn from them, and complete tasks without human intervention. This automation reduces human errors and intervention. Integrating AI and machine learning into agriculture has shown promise in various applications, making the sector more efficient and sustainable.

AI and Object Detection

AI-powered image recognition systems have revolutionized agricultural processes. 

One remarkable example is the smart sprayer developed by Blue River Technology. This innovative sprayer employs computer vision and AI to detect individual plants and weeds, enabling precise herbicide application. This technology can potentially reduce herbicide use by more than 90%, starkly contrasting traditional broadcast sprayers. Traditional methods often involve treating entire fields, even when specific areas do not require treatment. Using AI-based smart sprayers minimizes costs, lowers crop damage risks, decreases pesticide residue, and reduces environmental impact.

Plant and weed detection using AI(Blue River Technology)
IFAS

Another significant development in this area is the robotic strawberry harvester by Harvest CROO Robotics. This robot uses machine vision and AI to detect and locate ripe strawberries, addressing the labor shortage challenges strawberry growers face. Automation in harvesting not only reduces labor costs but also enhances the competitiveness of the industry.

 Harvest CROO Robotics (HCR) Strawberry Picking Robot
IFAS

AI and Disease Detection

Disease Detection System AI Approach Disease Detected
X-FIDO Program Deep Learning Convolutional Neural Network (DP-CNN) Olive quick decline syndrome (OQDS)
Grapevine Disease Detection System Deep Learning Grapevine Pierce's disease (PD) and grapevine yellows (GY)

Researchers at the Precision Agriculture Engineering program at UFL are working on AI-based systems to detect, categorize, and distinguish various agricultural objects. They are developing vision- and AI-based systems to identify citrus trees, fruit, and flowers and distinguish between weeds and pests. These systems use transfer learning, an AI approach, to adapt to scenarios where collecting extensive training data is challenging.

Vision-based pattern recognition and deep learning have become powerful tools for plant disease detection. AI can distinguish between diseases with similar symptoms, offering quicker and more accurate diagnoses. 

For example, Cruz (A researcher at UFL, 2017) developed the X-FIDO program, a vision-based system to detect symptoms of Olive quick decline syndrome (OQDS) with high accuracy. The X-FIDO program offers improved detection accuracy by leveraging deep learning convolutional neural networks (DP-CNN) and data fusion algorithms.

Screenshots of the X-FIDO program
UFL

Ampatzidis and Cruz (2018) have also developed AI-based disease detection systems, such as identifying grapevine Pierce's disease (PD) and grapevine yellows (GY). These systems can differentiate between diseases with high accuracy, outperforming traditional methods. Automating disease symptom detection is a game-changer for agriculture, reducing yield loss and ensuring sustainable practices.

Credit: Cruz, El-Kereamy, and Ampatzidis (Researchers @UFL, 2018)

AI and Pest Detection

Pest management is a critical aspect of agriculture, and AI is transforming how growers address this challenge. The Asian citrus psyllid (ACP) is a key pest in citrus farming, acting as a vector for citrus huanglongbing (HLB), a devastating disease. Visual pest detection is labor-intensive and costly. Dr. Ampatzidis and Dr. Stansly have developed a vision-based automated system to detect, locate, and count ACP in citrus groves. This system, mounted on a mobile vehicle, uses machine vision and deep learning convolutional neural networks (DP-CNN) to accurately detect and count ACP.

Credit: Dr. Ampatzidis and Dr. Stansly

AI in Precision Agriculture: The Future

The application of AI in agriculture is poised to revolutionize the industry by enhancing precision, reducing costs, and mitigating environmental impacts. AI-driven object detection systems are transforming how pesticides are applied, enabling precise targeting of pests and reducing chemical use. Automating tasks like strawberry harvesting and disease detection streamlines operations and improves competitiveness.

As technology evolves, AI's role in agriculture will expand. AI can be integrated with mobile hardware to create cost-effective pest and disease detection systems. These technologies enable more precise pesticide application, reducing costs and environmental impact. Furthermore, AI has the potential to advance mechanical harvesting and pruning technologies for fruit and vegetables, making agriculture more efficient and profitable.

Adopting AI and other emerging technologies in agriculture is not limited to pest and disease management. When combined with machine learning, big data analytics, and IoT, AI transforms farming practices. Drones are used for various tasks, including research analysis, safety, monitoring soil hydration, and yield problem identification. Farmers may gain important insights from the data gathered by these technologies, which will enable them to optimize their operations and make wise decisions.

AI in Agriculture Crop Management

As we move toward a global population of nearly 10 billion by 2050, the agriculture sector faces tremendous pressure to meet the world's growing food demands. This, coupled with environmental challenges, underscores the need for innovative solutions. Adopting AI, machine learning, and big data analytics in agriculture promises a second green revolution that can address these challenges and sustainably feed the planet.

Precision agriculture, enabled by AI, has the potential to transform traditional farming into a more efficient and sustainable industry. AI's applications are far-reaching, from automatically detecting drought patterns to monitoring the ripening of fruits like apples and tomatoes. Smart tractors are weeding out diseased and sick plants, while drones are extensively used in agriculture for research analysis, safety, and soil hydration monitoring.

Smart drones are crucial for pesticide spraying, targeting diseased plants precisely. They also monitor the physical and chemical properties of the soil, providing valuable insights for farmers. AI-driven applications facilitate disease detection and suggest corrective measures, making agriculture more efficient and data-driven.

Integrating AI and other emerging technologies into agriculture is not limited to pest and disease management. It encompasses livestock monitoring, fish farming, and smart greenhouses. Blockchain technology is also gaining traction in agriculture, and crop management ensuring transparency and accountability throughout the supply chain.

Technologies that Help Maximize Farm Yield

Technology Benefits Impact on Farm Yield
Artificial Intelligence (AI) Automates tasks, identifies patterns, optimizes operations. Increases efficiency, reduces errors.
Internet of Things (IoT) Monitors environmental conditions, provides real-time data. Enhances crop health and yield prediction.
Precision Farming Equipment Enables precise application of resources, minimizes waste. Improves resource management, boosts yield.
Automated Machinery Streamlines labor-intensive tasks, increases productivity. Reduces labor costs, enhances overall output.
Smart Sensors Provides real-time data on soil and crop conditions. Facilitates proactive decision-making, optimizes yield.
Drones and UAVs Enables aerial monitoring and precision application of inputs. Identifies issues, facilitates targeted interventions.

By adopting smart technologies and data-driven solutions, farmers can increase farm output significantly, with some estimates suggesting up to a sixfold increase per acre. This transformation empowers farmers to optimize every inch of their farmland. Traditional farming methods often leave parts of the farmland unused or wasted, but AI and big data analytics ensure every plant is scanned for health and growth tracking. Persistent pest problems are identified and notified to farmers, which was impossible with traditional agriculture methods.

The Future of Agriculture will be Technology-Empowered

AI technology has advanced to the point where we now have machinery that can identify and weed out infected plants, satellite imagery that reveals drought patterns, and even plant disease diagnosis apps like 'Plantix.' The future of agriculture is already being shaped by these technological advancements. AI is transforming the agricultural industry by giving farmers access to priceless insights that increase productivity and sustainability. 

The early adopters of these technologies will set the pace for agricultural transformation and food security for our expanding population in the years to come. A tech revolution in agriculture is about to happen, and those who embrace it will benefit from improved farm yields, higher earnings, and better quality of life.

FAQs about AI in Agriculture

Q1: How can AI increase accuracy and precision in agriculture?

AI utilizes data-driven insights to make more accurate decisions and optimize various agricultural processes, enhancing precision in planting, harvesting, and pest management tasks.

Q2: What technology is used in precision farming?

Precision farming relies on various technologies, including AI, IoT, GPS, smart sensors, and automated machinery, to optimize resource use and improve crop yields.

Q3: What are the applications of AI in precision agriculture?

AI applications in precision agriculture include crop monitoring, disease detection, pest management, and automated decision-making to enhance farm productivity.

Q4: What is machine learning in precision agriculture?

Machine learning in precision agriculture involves training algorithms to recognize patterns in data, enabling more informed decisions for tasks like irrigation and crop health assessment.

Q5: What is the role of robotics in precision farming?

Robotics play a crucial role in precision farming by automating labor-intensive tasks, such as harvesting and weeding, which results in increased efficiency and yield.

Last updated on: 
December 13, 2023
Author

Dipen Dadhaniya

Engineering Manager at Interview Kickstart

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AI in Agriculture: Precision Farming and Crop Management

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