In a way, successful farming comes down to making complex decisions based on interconnections of a multitude of variables, including crop specifications, soil conditions, climate change, and more. Traditionally, farming strategies have been applied to an entire field or its part at best. Machine learning in agriculture allows for much higher precision, enabling farmers to treat plants and animals almost individually, which in turn significantly increases the effectiveness of farmers’ decisions.
Let’s discover how agriculture can benefit from Machine Learning at every stage:
Species selection is a tedious process of searching for specific genes that determine the effectiveness of water and nutrients use, adaptation to climate change, disease resistance, as well as nutrients content or a better taste. Machine learning, in particular, deep learning algorithms, takes decades of field data to analyze crop performance in various climates and new characteristics developed in the process. Based on this data they can build a probability model that would predict which genes will most likely contribute a beneficial trait to a plant.
While the traditional human approach for plant classification would be to compare the colour and shape of leaves, machine learning can provide more accurate and faster results analyzing the leaf vein morphology which carries more information about the leaf properties.
Field Conditions Management
For specialists involved in agriculture, the soil is a heterogeneous natural resource, with complex processes and vague mechanisms. Its temperature alone can give insights into the climate change effects on the regional yield. Machine learning algorithms study evaporation processes, soil moisture, and temperature to understand the dynamics of ecosystems and the impingement in agriculture.
Water management in agriculture impacts hydrological, climatological, and agronomical balance. So far, the most developed machine learning-based applications are connected with an estimation of daily, weekly, or monthly evapotranspiration allowing for more effective use of irrigation systems and prediction of daily dew point temperature, which helps identify expected weather phenomena and estimate evapotranspiration and evaporation.
Yield prediction is one of the most important and popular topics in precision agriculture as it defines yield mapping and estimation, matching of crop supply with demand, and crop management. State of the art approaches have gone far beyond simple prediction based on the historical data, but incorporate computer vision technologies to provide data on the go and comprehensive multidimensional analysis of crops, weather, and economic conditions to make the most of the yield for farmers and population.
The accurate detection and classification of crop quality characteristics can increase product price and reduce waste. In comparison with human experts, machines can make use of seemingly meaningless data and interconnections to reveal new qualities playing a role in the overall quality of the crops and to detect them.
Both in open-air and greenhouse conditions, the most widely used practice in pest and disease control is to uniformly spray pesticides over the cropping area. To be effective, this approach requires significant amounts of pesticides which results in a high financial and significant environmental cost. Machine learning is used as a part of general precision agriculture management, where agrochemicals input is targeted in terms of time, place, and affected plants.
Apart from diseases, weeds are the most important threats to crop production. The biggest problem in weeds fighting is that they are difficult to detect and discriminate from crops. Computer vision and machine learning algorithms can improve the detection and discrimination of weeds at a low cost and with no environmental issues and side effects. In the future, these technologies will drive robots that will destroy weeds, minimizing the need for herbicides.
Similar to crop management, machine learning provides accurate prediction and estimation of farming parameters to optimize the economic efficiency of livestock production systems, such as cattle and eggs production. For example, weight predicting systems can estimate the future weights 150 days prior to the slaughter day, allowing farmers to modify diets and conditions respectively.
In the present-day setting, livestock is increasingly treated not just as food containers, but as animals who can be unhappy and exhausted of their life at a farm. Animals behavior classifiers can connect their chewing signals to the need in diet changes and by their movement patterns, including standing, moving, feeding, and drinking, they can tell the amount of stress the animal is exposed to and predict its susceptibility to diseases, weight gain, and production.
The value of smart automation is widely recognized across many sectors, proved by examples of AI in fintech or AI in real estate. In agriculture, this segment of technologies is becoming essential. With data at the core of farming decisions and the development of agrochemical products, the potential is immense. Perhaps, more importantly, machine learning is set to become a behind-the-scenes enabler of more sustainable use of natural resources and a huge contributor to a better environment.
However, for this technology to have a tangible impact on agriculture, it needs widespread recognition among stakeholders, a different mindset from farmers, and sufficient funding. This is a long-haul game. Companies need to be ready to reinvent themselves, learn new skills, and adapt to the rules imposed by big data.