Machine Learning in Agriculture

Machine Learning in Agriculture

For modern-day agribusinesses to stay relevant, it is necessary for farmers to implement a data-intensive approach. Machine learning helps analyse large sets of data, enables farmers makes better farm-related decisions, enhances crop productivity and eliminates food safety concerns. Also, through machine learning, other agribusiness functions such as evaluating crops, estimating yields, predicting diseases, etc. become easy.

Did you know that the Food and Agricultural Organisation of the United Nations stated that in 2030, about 660 million people may be suffering from hunger?

This increases the necessity to modify our agricultural practices that would satiate hunger issues at a global level. Data assessment and finding patterns in every aspect of food production is a key means to enhance food production. Through machine learning algorithms, agribusiness owners can assess several images, videos, data sets, and other farm-related data that help identify different trends. By implementing this data assessment method, farmers can select the right crop that would yield better results. Other than this, issues related to crop and soil health can also be predicted and resolved before time so that your farm’s productivity doesn’t get impacted.

In this blog, we have discussed

The Advantages of Machine Learning in Agriculture

  1. Determine the Right Time for Crops: For optimal crop yield, it is essential for farmers to sow and cultivate them at the right time. Also, choosing the right crop based on your soil type and season is a must. Hence, the first step of a successful agribusiness is making the right farm decision. As a farmer, you need to pick a crop depending on your field’s disease resistance, soil health, the crop’s ability to adapt, and fluctuations in weather conditions. This is where machine learning proves to be beneficial. Through machine learning, farmers can collect a huge amount of data that would help them estimate their crop productivity accurately. Machine learning is extremely beneficial for rotational farming with different seasonal crops.


  1. Crop Yield Patterns: Long-term information is always necessary. To synchronise your agricultural process, real-time data is a must. To gather real-time field data in the form of images and videos, drones or unmanned aerial vehicles are the perfect tools. These drones come with scanners and can monitor large areas of farmlands within just minutes. Through machine learning, farmers can get deep insights into crop health by combining long-term and real-time data. Using these done-captured images, farmers can compare the height of the plants, check for diseases and other such parameters that denote crop performances, which in turn will help them chalk out precise patterns that can predict the overall crop yield.


  1. Ensures Suitable Water Usage and Sustainable Irrigation Methods: One of the key functions of agribusinesses that helps enhance farm productivity while ensuring sustainability and cost-effectiveness is water management. To manage water aptly, you would require a rightly monitored irrigation system. Through machine learning, farmers and agribusiness owners can carry out irrigation practices hassle-free. Using past data and machine learning algorithms, farmers can understand the moisture requirements for a particular crop which in turn determines the right water usage, minimising unnecessary farm expenses. At the same time, issues such as over-irrigation leading to crop damage can be prevented through agricultural practices based on machine learning.


  1. Agribots Optimising Harvesting Functions: One of the significant costs that agribusiness owners need to incur is labour costs, especially when it comes to plantations or large farmlands. These become more expensive during peak harvest season. It is necessary for farm owners to not just hire labourers but also assign a skilled workforce to ensure there are no errors during harvest. Here, machine learning comes in handy as it solves the problem through agribots. These bots can identify the right harvest time and enhances overall farm performances.


  1. Maintaining your Farm Animal’s Health and Productivity: For managing livestock, companies that deal in animal farming and farmers face significant challenges concerning infrastructure, connectivity, and fodder for livestock. Tracking diseases and eliminating their spread is also a time-consuming and challenging process. Without adequate safety measures, livestock-rearing farmers will not be able to safeguard the health of their livestock. Implementing machine learning helps farmers analyse all such factors as movement and changes in diet. It can also make predictions related to weight gain for a specific animal based on previous data.


Wrapping Up,

Machine learning has been beneficial in smart data usage that helps capture specific traits related to agricultural production and management. This helps agribusiness owners build a productive and secure future. At the same time, machine learning helps enhance productivity while minimising its impact on the environment.

For a more detailed insight into machine learning and its implementation in your farming business, contact our experts at KG2 Australia who will assist you in making an informed decision. If you wish to know how machine learning has proven to be beneficial to Australian farmers, browse through our extensive website for detailed insights.