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From Farm to Table: How AI Drives Innovation in Agriculture

Walk through step by step how AI is driving innovation in agriculture, making it easier to grow, harvest, and deliver fruits from the farm to your table.

With the world's population expected to grow by 2 billion by 2050, we need about a 60% increase in food production to keep up. Artificial Intelligence (AI) is helping us meet this challenge by driving innovation in agriculture. AI innovations can be used to monitor livestock, analyze crop growth, predict when farm equipment needs maintenance, and so much more. When we think of agriculture, we often just picture farming. However, it's an umbrella that encompasses many different functions and processes.

To get a better understanding of the impact of AI in agriculture, Let’s take a specific example: the lifecycle of a fruit. What happens during its journey from a field to our tables?

In this blog, we'll explore how AI helps in every step of the process, from planting, growing, and harvesting to processing, transporting, and selling fruits. Let’s get started!

Embracing New Technology in Agriculture For Planting Fruits

The first step in the life cycle of fruit crops is selecting the seeds and planting them. A farmer needs to decide which fruit to grow for the season. AI techniques like machine learning can be used to analyze large amounts of data to help farmers choose the best seeds for their specific soil and climate conditions. By evaluating historical weather patterns, soil composition, and crop performance data, AI can recommend the optimal seed varieties that are most likely to thrive. AI being used for seed selection is an ongoing area of research with a lot of interest and potential. 

For example, the 2018 CGIAR Inspire Challenge awarded $100,000 to a project using machine learning for smarter seed selection. This project was led by researchers from the BioSense Institute and CIMMYT. They used data about different new maize varieties from hundreds of evaluation sites in Mexico to develop models that predict seed performance. Having a data-driven approach increases the chances of a successful harvest and reduces the risk of crop failure. Once the best seed type is selected, computer vision can step in to check the quality of the seeds.

Fig 1.  A satellite image showing maize production in Mexico, where brighter areas indicate healthier vegetation.

Using Computer Vision To Evaluate Seed Quality

Computer vision can be used to analyze high-resolution images of fruit seeds to detect imperfections, diseases, and genetic traits that may not be visible to the human eye. Various computer vision tasks can be used to analyze these images to sort, grade, and assess the quality of the seeds. By automating these tasks, AI can help guarantee that only the highest quality seeds are planted, and farmers can achieve better crop yields.

For instance, Seed X's GeNee™ Sorter is an AI-powered seed sorter that improves the seed selection process. The sorter divides the seeds into two categories: high-quality seeds that are likely to germinate are directed into the primary box, while seeds that are unlikely to germinate are sorted into a secondary box. It handles tasks like assessing color, shape, size, genetic purity, and predicting germination rates. Thanks to the sorter, germination rates can be boosted to over 90%, which means more seeds successfully grow into healthy plants.

Fig 2. Seed X's GeNee™ Sorter.

Using AI to Grow and Harvest Fruits

AI can also be used to improve soil analysis and crop monitoring. Drones with advanced cameras fly over fruit fields, capturing detailed images of soil and plant health. These images are processed to create maps showing variations in soil moisture, nutrient levels, and plant health. Based on insights from image analysis, tasks like weed detection, growth monitoring, yield estimation, adjusting irrigation, applying fertilizers precisely, and implementing targeted pest control can be done. Real-time monitoring using AI can help improve fruit crop yields and promote sustainable farming practices.

Harvesting a field just one day before or after the optimal time could reduce the farmer's potential income by 3.7% to as much as 20.4%. AI can help determine the best moment to pick the fruits. Traditional harvesting methods rely heavily on manual labor and can be less efficient and more time-consuming. AI-assisted harvesting methods use advanced sensors and machine learning algorithms to analyze data on fruit color, size, and environmental conditions to predict ripeness. This way, fruits are harvested when they're at their best, leading to higher yields and less waste.

Fig 3. Using Computer Vision To Segment Ripe Strawberries.

Post-Harvest Processing: Volumetric Analysis of Fruit Segmentation and Packaging

After harvesting, the next key step is sorting and grading the fruits so that the best produce can reach consumers. AI can be used for the volumetric analysis of fruit segmentation. By applying computer vision models like Ultralytics YOLOv8, a fruit's size, shape, and quality can be assessed.

Fig 4. Using the Ultrlaytics YOLOv8 computer vision model to segment fruits.

The process involves capturing high-resolution images of fruits on a conveyor belt, segmenting individual fruits using the YOLOv8 model, and performing volumetric analysis to measure size and shape and detect defects. Based on the analysis, fruits are automatically sorted and graded into different categories for appropriate packaging, processing, or distribution. AI-powered sorting and grading improves efficiency, accuracy, and consistency, reduces waste, and maximizes the value of the harvest.

Once fruits are sorted and graded, automated packaging machines can precisely pack them. AI-driven OCR (Optical Character Recognition) systems can check the packaging for accuracy, verifying labels, barcodes, and other important information for compliance with standards and regulations. By inspecting packaging in this manner, situations like expired produce can be avoided. The AI can detect incorrect labels or expired dates and flag these for correction before the products reach consumers.

AI Innovations in the Retail and Distribution of Fruits

Now, let's consider you have the best fruits packaged and ready to deliver. AI can improve the logistics and transportation of fruits by optimizing routes. Keeping fruits fresh during transit and finding the most efficient delivery routes are major challenges. AI algorithms can analyze traffic patterns, weather conditions, and delivery schedules to determine the best routes and reduce travel time and costs.

After the fruits reach the store, AI continues to play a crucial role in inventory management and demand forecasting. AI systems can analyze sales data, customer preferences, and seasonal trends to predict demand more accurately. Retail stores can maintain optimal inventory levels based on the insights from AI and reduce the risk of overstocking or running out of stock. 

Fig 5. Staff restocking fruit at a retail store. Image source Envato Elements.

Computer vision can be used in stores to monitor shelf stock in real-time. Using object detection, AI-powered cameras can identify when stock is low or improperly placed and alert staff to restock or rearrange the shelves. By ensuring that the right amount of fresh produce is available at the right time, AI helps improve customer satisfaction and reduce food waste.

The Overall Impact AI Has On the Fruit Industry

AI has the potential to play a big role in helping consumers get better quality fruits. A great example of successful AI integration in the fruit industry is Nature Fresh Farms. Nature Fresh Farms has transformed its operations from seed to store using AI technology. AI-powered sensors and data analytics help track and manage everything from climate and humidity levels in the greenhouse to the logistics of transporting produce. Nature Fresh Farms has optimized growing conditions, controlled irrigation, and reduced costs. The AI system is also able to get produce to reach supermarket shelves within 24 - 48 hours of packaging, significantly reducing transportation time and maintaining freshness.

Despite its many benefits, there are some potential drawbacks to using AI in the fruit industry:

  • Cost of implementation: The initial investment in AI technology can be high, which may be a barrier for some farmers and producers.
  • Technology dependency: Relying heavily on AI and technology can create a dependency that may be challenging to manage, especially if there are technical issues or failures.
  • Maintenance and upkeep: AI systems require regular maintenance and updates to function effectively, which can add to ongoing costs and operational complexity.
  • Complexity of use: Some farmers may find AI technology difficult to understand and use, making training and support necessary.

Cultivating Tomorrow

From the selection of seeds to the ripening of fruits, AI is changing agriculture throughout the lifecycle of produce, from farms to your dining table. It helps farmers monitor soil health, predict the best times to harvest, and sort produce accurately. By optimizing resources, reducing waste, and improving crop yields, AI makes farming more efficient and sustainable. Despite challenges like costs, the need for technical expertise, and the dependency on data quality, the advantages of AI make these challenges worthwhile in many cases. That’s why more and more farmers are adopting AI.

Stay connected with our community to keep learning about AI! Check out our GitHub repository to discover how we use AI to create innovative solutions in industries like manufacturing and healthcare. 🚀

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