Over the past few years, machine learning has evolved alongside other big data technologies and advanced computing to transform industries around the world, and agriculture is no exception.
With the advancement of machine learning, machine learning in agriculture has been able to solve many problems encountered by the industry.
Only by continuously surpassing competitors in decision-making can enterprises succeed, and agriculture is no exception. Through machine learning in agriculture, farmers now have access to more advanced data and analytical tools that facilitate better decision-making, increase productivity, and reduce waste in food and fuel production while reducing adverse environmental impacts.
With the assistance of high-precision algorithms, the concept of "smart agriculture" continues to develop, improving the efficiency and productivity of agriculture. Machine learning is a branch of science that allows machines to learn without being explicitly programmed, which is the mechanism behind it. To open up new possibilities for unraveling, analyzing and understanding data-intensive processes in agricultural organizational settings, machine learning has evolved alongside big data technologies and powerful computers. Farmers can now use sensors on their farms to predict agricultural yields and assess crop quality, identify plant species, and diagnose plant diseases and weed infestations based on machine learning-enabled electronic innovations. This seems to be an unimaginable level. Machine learning plays a prominent role in agriculture throughout the planting, growing and harvesting process. It starts with sowing seeds, progresses through soil testing, seed breeding and water supply measurements, and ends with robots collecting the harvest and using computer vision to assess its maturity. Today, the amount of data available to farmers without the help of machine learning technology is overwhelming. ML can quickly evaluate large amounts of data and recommend the most profitable strategies with the help of this data. For example, ML can recommend when to plant to prevent pests and diseases. The advantages of digital agriculture are legitimate and can help growers make optimal input decisions to increase yields and profits. Additionally, farmers can be helped to determine actual costs on a field-by-field basis, not just across the entire farm.
In recent years, machine learning has been widely developed in the agricultural field. The following are its applications in agriculture:
The difficult process of species selection requires the search for specific genes that guarantee efficient responses to water and nutrients. Ideal plant varieties will be resistant to climate change, disease resistant, more nutritious and taste better.
To thoroughly investigate crop performance, machine learning allows us to extract information from decades of field data. The data were used to create a probabilistic model that predicts which traits would give the plant a desirable genetic advantage.
Species identification of crops is usually done through simple comparisons, such as leaf color and shape. Using more advanced methods, such as evaluating leaves with the help of vein morphology, machine learning allows us to evaluate plants in a more sophisticated, accurate and fast way.
Machine learning algorithms examine evaporation dynamics, soil moisture and temperature to understand ecosystem processes and their impact on agriculture.
Defects in soil can be addressed through ML strategies. For example, machine learning technology can help farmers maintain optimal amounts of inorganic nitrogen. Predict nitrogen cycling in soil and environment with nitrogen models to guide farmers to achieve optimal levels. Software simulations can detect whether nitrogen is available and determine when to add it to the soil. In addition, it can notify farmers when nitrogen levels are too high to avoid damage to crops.
Irrigation systems can also be used more efficiently, thanks to ML-based applications. It can estimate daily, weekly or monthly evaporation and predict daily dew point temperature, which helps predict expected weather events and calculate evapotranspiration and evaporation.
One of the most important and well-known areas of precision agriculture is yield forecasting, which includes the mapping and assessment of yield, matching crop supply and demand, and crop management. Modern methods go far beyond simple forecasts based on historical data, incorporating computer vision technology to provide data instantly and provide comprehensive multi-dimensional analysis of crops, weather and economic conditions to maximize yields for farmers and the public at large.
Accurately identifying and classifying the quality attributes of agricultural products can increase product prices and reduce waste. Machines can leverage seemingly meaningless data and connections compared to human experts to reveal and discover new attributes that contribute to the overall quality of a crop.
To control diseases, large amounts of pesticides must be sprayed on crop areas, which often results in high economic costs and considerable environmental impact. When using general precision agriculture management, ML is the targeted use of pesticides based on time, location and plants that will be affected.
Weeds pose a serious threat to the growth of crops. Weeds are difficult to distinguish from crops, which is the biggest challenge in weed control. Computer vision and machine learning algorithms in agriculture can enhance weed identification and discrimination with minimal expense and no negative impact on the environment. Future models of this technology will power weeding robots, minimizing the need for herbicides.
In order to maximize the economic benefits of livestock production systems, such as the production of cattle and eggs, machine learning enables accurate predictions and predictions on agricultural aspects. For example, 150 days before slaughter, a weight prediction system can predict future weight, allowing farmers to adjust their diet and environmental factors accordingly.
Today’s livestock are increasingly seen as unhappy and exhausted animals in farm life, rather than just as food carriers. An animal's movement patterns, such as standing, moving, eating and drinking, can determine how stressed the animal is and predict its susceptibility to disease, weight gain and productivity. An animal's chewing signals can be linked to the need for food adjustment.
Agricultural machine learning is not some mysterious gimmick or magic trick. Rather, it is a carefully designed set of models used to collect specific data and employ methodologies to achieve desired results.
Artificial Neural Network (ANN) and Support Vector Machine (SVM) are two very popular machine learning models used in agriculture.
ANN is a model of biological neural network that can simulate complex activities such as pattern production, reasoning, learning and judgment. It is inspired by how the human brain works.
SVM is a binary classifier that uses linear separation hyperplanes to divide data instances into categories. Clustering, regression and classification are performed using support vector machines. They are used in agriculture to estimate animal yields and crop productivity and quality.
Additionally, a farmer chatbot is under development. The robots are expected to be smarter than consumer-facing Alexa and similar assistants because they can not only provide numbers but also evaluate data and advise farmers on complex issues.
How machine learning is revolutionizing agriculture
Machine learning breakthroughs have incredible potential, just like software. Agricultural scientists are testing their theories on a larger scale and helping to develop more accurate, real-time predictive models related to crops. Machine learning in agriculture has the power to provide more solutions for sustaining the world’s population, combating climate change, and protecting natural resources.
Currently, machine learning solutions focus on specific problems, but as automated data collection, analysis, and decision-making are further integrated into connected systems, many agricultural activities will transform into what is known as knowledge-based agriculture, which will be able to improve Yield and product quality.
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