What You Need to Know About AI in Agriculture

Dr. Vijayalaxmi Kinhal

February 19, 2024 at 5:16 pm | Updated February 28, 2024 at 6:01 pm | 7 min read

  • AI, in combination with other technologies, is solving several modern problems caused and faced by industrial agriculture.
  • AI applications are increasingly used in agriculture for soil and crop monitoring, farm operations, farm management, supply of food, and crop research.
  • AI solutions in agriculture can improve productivity with less resource use and reduce environmental impacts.
  • Integrating AI-driven climate-smart technologies into agriculture holds great promise for building resilience, mitigating climate risks, and ensuring sustainable food production for future generations.

AI (Artificial intelligence) or algorithms transform agriculture by providing growers with actionable insights from combined information from fields, crops, and weather. The numerous decisions that growers must make within any crop cycle are increasingly covered by AI assistance.  Overall, AI enhances efficiency, productivity, and sustainability in agriculture.

AI Improves Decision-making

AI gives growers access and makes sense of vast volumes of Big Data through analytics, allowing farmers to make informed data-driven decisions that can optimize each stage and aspect of food production. It would be impossible for any individual to read or analyze the data processed by AI.

AI can involve machine learning and deep learning models and is combined with various other technologies like computer vision, near-infrared (NIR) spectroscopy, robotics, Internet of Things (IoT), and remote satellite imagery.

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AI solutions offer several benefits in agriculture:

  1. Cost minimization: AI optimizes farming practices, reducing costs through early pest detection and smart resource allocation.
  2. Increased ROI: Automation of tasks like planting and harvesting boosts efficiency, while precision farming optimizes resource use, leading to higher returns on investment.
  3. Labor shortage relief: AI assists in analytics and planning, helping farmers cope with labor shortages and providing new agriculture expertise where needed.
  4. Advanced risk management: AI analyzes historical data for accurate predictions, empowering farmers to adapt strategies to weather and market changes and minimize losses.
  5. Resource optimization: AI provides early insights into developing crop issues, like nutrient deficiency or biotic stress, so that growers can proactively reduce resource usage, minimize waste, and contribute to sustainable farming practices.

The global market size for AI in agriculture is expected to grow at a CAGR of 23.1% between 2023 and 2028, from 1.7 billion to 4.7 billion dollars. Significant advances have been achieved in crop and soil monitoring, plant protection, precision irrigation and fertilization, supply chain resilience, and farm management.

1. Soil Monitoring AI in Agriculture

AI-driven soil monitoring touches on several aspects of soil utility in agriculture.

AI-based soil monitoring integrates satellite imagery, field sensors, machine learning, IoT, and computing to analyze soil conditions, including nutrients, moisture levels, and composition. This gives farmers real-time insights to make informed decisions about crop selection, fertilization, crop rotation, erosion control, soil degradation prevention, and sustainable land management practices.

A recent development is tracking soil carbon stocks, which comprise 75% of all carbon in terrestrial ecosystems. However, agricultural practices like plowing, fertilizers, and pesticides impact carbon cycling and release carbon. Measuring and monitoring soil carbon is challenging but crucial to managing soil stocks. Traditional ground sampling is slow and costly, limiting its feasibility. Remote sensing, combined with machine learning, offers a promising solution. Machine learning models can predict soil carbon levels across vast areas by analyzing factors like climate, vegetation, and soil properties from a distance. Landowners should be able to measure their soil carbon levels and track changes over time, empowering them to manage their land more effectively.

Figure 1: Image by Sarah Clarry from Pixabay

2. Crop monitoring

Integrating AI and advanced technologies like drones, NIR sensors, and computer vision has revolutionized various aspects of agriculture, particularly optimizing crop growth, monitoring maturity, and automating produce grading and sorting.

  1. Crop Growth Monitoring: Traditional methods of monitoring crop growth relied heavily on human observation, which was neither accurate nor timely. However, using remote imagery and drones equipped with cameras and AI-powered computer vision models has enabled farmers to monitor crop and soil conditions more efficiently, see Figure 1. By analyzing aerial image data, these systems can provide valuable insights into crop nutrition and growth patterns, allowing farmers to make data-driven decisions to optimize production efficiency using fertilizers only in necessary quantities and areas.
  2. Estimating Crop Maturity: Estimating crop growth and maturity is a complex task requiring significant labor and expertise. AI-powered hardware, such as NIR sensors and image recognition tools, can accurately detect and track crop changes, enabling farmers to predict when crops will reach optimal maturity. Studies have shown that AI-based methods outperform human observers in predicting crop maturity, leading to higher accuracy rates, potentially significant cost savings for farmers, and ensuring optimum produce quality to meet consumer satisfaction.
  3. Produce Grading and Sorting: After harvest, AI computer vision and NIR sensors can assist farmers, packers, and suppliers in sorting and grading produce. These technologies can automate the process by inspecting fruits and vegetables for size, shape, color, and volume, enabling accurate sorting of “good” produce from defective ones. Automated sorting systems powered by AI offer higher accuracy rates and faster processing speeds than manual sorting, leading to increased efficiency and reduced labor costs for farmers.

3. Precision Irrigation

AI algorithms and smart equipment enable precision irrigation to tackle water scarcity and optimize resource management. Through data analysis from various sources, such as weather forecasts and soil moisture sensors, AI optimizes irrigation schedules, minimizing water wastage and ensuring optimal plant hydration.  Additionally, AI-driven systems conserve energy by scheduling irrigation during off-peak hours and adapting to changing climate conditions.

AI in agriculture enhances water-use efficiency, resource conservation, labor optimization, and adaptability to climate change, fostering sustainable and resilient farming practices.

4. Plant Protection

Advancements in plant health sensors, coupled with AI algorithms, can improve crop protection management. AI-powered pesticide application optimizes effectiveness while minimizing ecological impact by analyzing the infield difference in plant health due to various stressors like weeds, pests, and diseases. AI-driven crop monitoring provides valuable insights, enabling variable rate application decisions for weed, disease, and pest control, leading to increased efficiency and sustainable practices. Additionally, app-based solutions support farmers in developing countries, offering early disease detection and tailored advice, enhancing agricultural resilience and productivity.

5. Farm Equipment

Integrating AI into agricultural equipment offers many benefits, from increased efficiency to reduced environmental impact; however, these can be expensive.

  • Autonomous tractors and farming equipment, for example, promise greater precision and productivity by leveraging AI and computer vision to perform tasks traditionally done by humans. This reduces labor costs and optimizes using resources like fertilizers and pesticides.
  • Agriculture drones and robots with AI capabilities offer farmers a powerful tool for managing their fields more effectively. Drones can quickly survey large areas and provide high-resolution imagery, which AI algorithms can analyze to identify areas needing attention, such as crop stress or weed infestations. Robots, on the other hand, can perform labor-intensive tasks like weeding or pruning with precision and efficiency, further reducing the reliance on manual labor.
  • Chemical application machinery is becoming efficient where AI is being used to target specific plants or weeds, minimizing the use of pesticides and herbicides while still effectively managing crops.

With new AI-powered equipment introduced each year, the agricultural industry is on a path of rapid transformation.

6. Supply Chain Resiliency

AI technologies are also being applied to enhance the resiliency of agricultural supply chains.

Platforms that leverage predictive analytics and machine learning to detect disruptions in advance are incredibly valuable for the industry. By providing customized insights on climate, market,  and economic risks for various commodities and regions, they empower agricultural stakeholders to make informed decisions on marketing, harvesting, and storage to mitigate potential challenges.

These platforms optimize logistics and inventory management, reduce wastage, and ensure a steady supply of fresh produce, which is crucial for efficiently meeting market demands. Moreover, they promote sustainable practices and responsible sourcing by offering transparency into factors affecting suppliers, including environmental, social, and governance (ESG) considerations.

Quality control with portable devices from Felix Instruments Applied Science can provide a standard for quality comparison throughout the supply chain and is available on-site for real-time monitoring of fresh produce quality for optimum ripening, storage, and retailing.

7. Plant Breeding

It is possible to leverage precision technology integrating AI to accelerate breeding new crop varieties, particularly in response to the challenges posed by climate change. A project can streamline the process of developing climate-resilient crop varieties by utilizing an app or precision tools for phenotyping, which involves observing and analyzing plant characteristics.

Traditionally, it can take up to a decade to breed and introduce a new plant variety. However, given the rapid pace of climate change, more is needed to ensure crops adapt to evolving environmental conditions.

Apps and precision tools that evaluate plant morphological, physiological, and biochemistry can collect real-time data. External AI algorithms analyze this data to identify genetic traits that are well-suited to specific locations and are likely to confer resilience to anticipated changes in climate.

8. Indoor Farming

AI-driven microcontrollers and automated systems aim to regulate environmental factors and optimize vertical agriculture crop growth and resource efficiency. While the potential benefits of vertical farming are significant, such as reduced water usage, land conservation, and year-round production, navigating the transition from traditional farming methods to technology-driven approaches can be challenging.

The challenges faced by some agtech startups highlight the complexities involved in integrating these technologies into practical and sustainable farming solutions.

9. Climate-Proofing AI in Agriculture

Leveraging AI-driven technology to climate-proof agricultural operations is crucial for ensuring food security and sustainability in the face of climate change.

  • Avoid risks: Using historical climate data and advanced algorithms, farmers can make informed decisions about when to plant, irrigate, and harvest crops, thus minimizing risks associated with extreme weather events and shifting climate patterns.
  • Optimize dwindling resources: Moreover, AI can assist in optimizing resource usage, such as water and fertilizers, by providing real-time insights into soil moisture levels, nutrient requirements, and crop health. This improves efficiency and conserves natural resources by minimizing unnecessary inputs.
  • Climate resilience planning: Farmers can adapt to changing environmental conditions while maintaining productivity and profitability by adopting more resilient crop varieties through benchmarking and precision agriculture techniques.

Felix Instruments Uses AI for Spectroscopy

Felix Instruments produces near-infrared spectroscopy that collects complex spectral data analyzed by chemometrics to give easy-to-understand estimations of many quality parameters that can guide decisions for harvesting, sorting, grading, culling, price determination, and regulation compliance. The F-750 Produce Quality Meter and the F-751 series are used in farms, the entire supply chain, and research laboratories for plant breeding and agricultural research.


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