How can AI help the Agriculture sector? webp image

Artificial Intelligence has impacted different sectors by providing great benefits. Agricultural industry is no different, with a variety of fields that have changed. With the increased need for more food across the World, it became more critical to introduce sustainable farming practices and make farms robust to different diseases, pests, climate disasters like droughts, or even going more bio via reduced usage of pesticides.

In this blog post, we will discuss a variety of fields of agriculture in which AI tools and algorithms can be applied, the benefits they offer, and the challenges that must be faced.

Precision Farming


Identification of stressed zones in crop monitoring software by Agremo

Precision Farming uses advances in remote sensing, GPS, and IoT to provide farmers with data-driven insight to make their production more efficient. Advances in this branch are mostly driven by the integration of Artificial Intelligence and drones, which allow data collection with data-driven decisions and time-efficient automation in conventional farming.

Satellites or Drones with advanced sensors can be used for crop health monitoring across large areas with real-time data like moisture levels and vegetation health. A good example of using a data-driven approach in precision agriculture is reducing usage of pesticides by using the Precision Spraying Method instead of conventional Broadcast Spraying (even by 90%).

Additionally, using smart sensors may allow monitoring of areas which are at risk of erosion, consequently predicting usage of certain elements and providing suggestions on targeted fertilization. Apart from the benefits of reduced amounts of fertilizers, farmers can reduce their costs and increase quality of their final product and even crop growth.

Another usage of precision farming is controlling crop yield, predicting and optimizing them. With that information, farmers can plan harvest logistics and estimate revenues. Precision farming can also assess soil health and recognize places at risk of erosion.

The main challenges in this area are:

  • Initial cost: For rural and small scale farmers, the initial cost of such solutions can be too large or inaccessible.
  • Specialized Training: Operating drones and AI systems requires specialized training.
  • Adoption among farmers: Farmers may hesitate to move from conventional ways, which worked before and trust AI-based systems.
  • Weather Conditions: Strong winds or heavy rains may make it impossible to use drones or provide the same level of predictions. On the other hand, areas like deserts may also have dust and extremely high temperatures, which must be considered.

Pest and disease detection


Visualisation of pest and disease by Fermata

Another area that has benefited from advances in Computer Vision is pest and disease detection. These models can detect pests or diseases early and limit the number of affected areas. Using such systems can reduce pesticide usage, lower operational costs, improve agricultural production and limit the risks of infestation outbreaks.

Possible applications include systems that monitor plantations and detect pests and diseases (e.g., the Fermata platform), machines that detect and remove weeds (e.g., the Laserweed), or mobile apps that act as plant doctors (e.g., Plantix).

Additionally, there is also the possibility of detecting dangerous insects based on a camera feed or the presence of certain frequencies in audio. A good example of such an application is the acoustic sensor and mobile app by, which allows farmers to detect the early onset of tree pests.


Laserweed from Carbon Robotics

The main challenges of this area are:

  • Data Quality and Data Shift: With the rapidly changing climate and the difficulty of getting good data quality, it is challenging to produce meaningful results.
  • Need for Human Oversight: Even though systems can improve the current situation, it is still important to keep a human in the loop to be vigilant of false positives and negatives.
  • Cost for custom solution: Few AI pest control solutions work for every problem, so each problem requires a custom solution.

Automatic Quality Control


Salad sorting machine from Quadra

Once harvested, it might be necessary to check which parts of the fruits can be sold, which should be used to feed animals, and which should be discarded. Here comes again a system using Computer Vision in multiple frequencies to find all possible defects, basing its decision not only on visible features but also on other characteristics (e.g., chlorophyll level).

The benefits of using Artificial Intelligence quality control systems are immense, from the high accuracy of defect detection to reducing the need for human labourers (who are hard to find and expensive) and their specialised training.

Challenges in that field are:

  • Availability of the data: Most companies are working on their private datasets, which often are not transferable to different regions due to the specificity of certain species of fruits/vegetables. Much data collection and labeling are required to create such a dataset.
  • Seasonability: Some defects might happen in certain seasons, and some might not.
  • Anomalies: Some defects are so rare that it is hard to detect them correctly.

Livestock Monitoring

Another area that can benefit from advances in AI is Livestock Monitoring. Thanks to applications in this field, controlling your farm animals might be significantly easier and require less staff while providing higher quality products (i.e., eggs, meat, etc.). Additionally, apart from counting and weighing animals based purely on a feed from cameras, it is also possible to detect anomalies in their behavior early or even observe their social interactions by combining feed from cameras (including both visual light and infrared), audio (i.e., mating sounds) and other sensors. Solutions like this can be seen on large cattle farms in America.

The main challenges of this area are:

  • Quality of data: Given changing behaviours, various animals, and their habitats, it might be difficult to create systems that will generalise well.
  • Initial cost: To our knowledge, few solutions can be used widely, so applications in that field will need custom solutions.
  • The multimodal character of data: As mentioned before, to get full information, it might be necessary to use different modalities (i.e., vision, audio) and merge them together, which might be challenging and require more high-quality data.


AI technology is enabling farmers change traditional farming methods to more efficient agricultural practices, introduce more sustainable food production, reduce physical labor and even slow down climate change. Opportunities to apply Artificial Intelligence in agriculture are almost limitless, and we already experience many benefits. In this blog post, we covered applications of precision farming that allow for data-driven decisions thanks to advances in remote sensing, IoT, and drones. Additionally, we went over systems that detect pests and diseases while growing. Then, we explored quality control systems after the harvesting, and finally, we covered livestock monitoring.

Reviewed by: Adam Wawrzyński

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