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ML in Agriculture: Revolutionizing Farming Practices

Last updated on: 
December 27, 2023
Abhinav Rawat
The fast well prepared banner
About The Author!
Abhinav Rawat
Abhinav Rawat
Product Manager at Interview Kickstart. The skilled and experienced mastermind behind several successful product designs for upscaling the ed-tech platforms with an outcome-driven approach for skilled individuals.

Machine learning and artificial intelligence are the current trends in the tech world. These sciences have been innovating almost every sector in the world, from businesses to education to transportation. From the beginning of time, agriculture has been a revolutionary sector. The sprinkle of AI and machine learning in agriculture has been a great venture. With the advent of new technologies, the global agricultural market has grown to $13,398.79 billion in 2023 and is expected to grow at 9.1% CAGR

Learn how AI is revolutionizing the agriculture industry and explore its applications in detail. 

Here is what we will cover in this article:

  • Machine Learning for Agriculture
  • Precision Agriculture: Applications of Machine Learning in Agriculture
  • Species identification
  • Weed and disease detection
  • Water management
  • Farm animals welfare
  • Yield management
  • Machine Learning Models and Algorithms Used in Agriculture
  • Predictive analysis
  • Deep learning
  • Computer vision
  • Neural networks
  • Gear up for Your Next Machine Learning Interview
  • FAQs on Machine Learning in Agriculture

Machine Learning for Agriculture

Machine learning (ML) is the means that helps computing devices imitate human intelligence, modify, and evolve in response to inputs. The use of machine learning in agriculture has many kinds of benefits for agricultural productivity. 

ML algorithms continuously examine data from cameras, sensors, and previous patterns. It helps generate priceless insights that can aid farmers make choices and develop their agricultural techniques. 

The developing concept of "smart farming" increases the effectiveness and production of agricultural produce with the use of highly refined algorithms. Its effects range from giving pesticide recommendations and estimating crop yields to streamlining plant breeding and anticipating production expenses.

ML for agricultural purposes

Integrating sensors with ML-enabled technological developments in their farms, farmers are able to calculate agricultural productivity while assessing crop quality. They can additionally identify plant varieties and detect plant diseases and invasive weeds at levels that initially seemed incomprehensible. 

Machine learning in agriculture is prevalent across the full cycle of cultivating, growing, and harvesting. It begins with planting a seed, continues with soil analysis, seed rearing, and water supply estimation, and finishes with robots harvesting the crop and determining its level of maturity using computer vision. 

Precision Agriculture: Applications of Machine Learning in Agriculture

Precision agriculture is an agricultural management strategy that collects and analyzes data regarding crops, soil, atmospheric conditions, and other variables affecting plant growth using the latest innovations, including AI and ML. By using these data points, farmers may decide how best to use supplies like water, fertilizer, and pesticides that are customized for every crop's and field's particular requirements. 

When it deals with cultivating crops, it's focused on improving agriculture's control and accuracy. The outcomes can be achieved with the help of technologies like GPS, GIS, remote sensing, satellite images, field mapping, AI, and ML to keep track of farming conditions, provide accurate forecasts, and enhance crop productivity and quality. 

Precision agriculture systems enable farmers to oversee any agricultural operations from a remote location. These systems have the potential to effectively manage significant regions, which makes them ideal for small-scale farms with big fields or numerous small plots of land. Implementing these systems significantly improves crop efficiency and increases yield while also saving money.

The application of machine learning in agriculture is vastly spread among the different domains of farming. Let’s have a look at the major uses of machine learning in agriculture.

Applications of machine learning in agriculture

Species Identification

The most desirable plant species can tolerate climate change, combat diseases, possess additional vitamins and nutrients, and have an improved taste. Machine learning gives us the ability to gather insights from years of agricultural data and use it to conduct a complete analysis of crop performance. The data is then used to build a probability model that anticipates the characteristics that will yield a plant with a significant genetic benefit.

Species identification in plants has usually been done through simple comparisons using indicators like the color and form of the leaves. Machine learning allows us to study crops in a significantly more intelligent, precise, and efficient manner. It utilizes more advanced techniques, like screening leaves with the application of vein anatomy.

Weed and Disease Detection

ML is used for targeting the distribution of agricultural chemical products based on the location, time, and crops that are going to be affected while implementing conventional targeted agricultural management techniques.

Weeds are challenging to recognize and differentiate from crops, and this presents the most challenging issue in weed control. Computer vision and machine learning algorithms in agriculture can improve the detection and discrimination of weeds with little cost and no adverse environmental effects. The demand for herbicides will be reduced as future versions of this science power weed-eating robots.

Water Management

Conventional agriculture often uses huge quantities of water, which has caused an adverse effect on the ecosystem. Farmers can use machine learning-enabled remote sensing devices to keep track of soil moisture levels or install intelligent sensors that recognize whether the plants require more water. 

These techniques might decrease overall agricultural expenses, increase the effectiveness of water use, and prevent resource wastage. Additionally, based on particular soil types and environmental factors, machine learning can be applied to identify crops that are resistant to drought and determine the most effective planting strategies. 

Farm Animals Welfare

Machine learning offers accurate predictions and modeling of farming factors to enhance the economic value of systems for livestock breeding, such as the rearing of cattle and eggs. 

Animals' motion patterns, such as sitting or standing, moving around, eating, and drinking, may demonstrate how much stress an animal is under and estimate its vulnerability to diseases, gaining weight, and output. Animals' chewing signals can be associated with the requirement of diet modifications.

Yield Management

One of the most essential and widely recognized features of precision agriculture is yield estimation. It includes yield mapping and evaluation, crop availability and demand coordination, and crop management. 

By utilizing computer vision technologies to offer data promptly and full multimodal assessments of produce, weather conditions, and market conditions, farmers can increase output for both individual farmers and the general public.

Accurately identifying and categorizing agricultural quality traits can raise product prices and reduce wastage. Machines are able to discover and identify novel features that contribute to the overall worth of crops by using relatively insignificant data and associations.

Machine Learning Models and Algorithms in Agriculture

To incorporate smart farming using machine learning, one needs to understand the work behind the technologies they use to make informed decisions. Here are some of the algorithms and models that are used for machine learning in agriculture:

Machine learning models and algorithms in agriculture

Predictive Analysis 

With the help of predictive analytics technologies, farmers are able to accurately predict crop yields, estimate consumer demand for certain products, and use fertilizer and irrigation systems more effectively. 

Predictive analytics can offer findings that may help farmers make better decisions regarding when they should plant, how to provide care for what they grow, and what pricing they should charge for their agricultural products by looking at previous data records. Furthermore, using predictive analytics, agricultural pests and diseases can be detected at an early stage. This enables farmers to take preventative action and minimize or avoid any possible damage.

Deep Learning

Deep learning is a branch of machine learning that uses artificial neural networks as its basis for learning. Deep learning frameworks gain insight from data in a manner analogous to human beings. This enables them to forecast outcomes using data that is too complicated for conventional machine learning procedures. 

The use of deep learning in smart farming can be used to forecast crop yields, diagnose pests and illnesses, and improve irrigation systems. Additionally, deep learning might help in the development of new crop varieties that have become more tolerant of changing climates.

Computer Vision

Computer vision is a tool that can recognize objects, record movements, and quantify features. It includes an extensive list of potential uses in smart agriculture, ranging from predicting yields and monitoring crops to targeted irrigation and autonomous farming equipment. 

One of the most significant uses of computer vision for agricultural purposes is crop health tracking. Farmers can identify early indications of disease or stress that might not be readily apparent to the human eye by studying photographs of crops. This data can help you decide when and how to use pesticides or perform other preventative steps.

Neural Networks

Since neural networks can be trained to detect patterns in data that may be too complicated for normal people to recognize, they are highly suited for agricultural applications. Artificial neural networks (ANN) have the potential to produce new crop varieties that tend to be more resistant to diseases and pests. 

By examining past harvest performance, moisture levels in the soil, and climate data, a neural network can be trained to forecast the best watering schedules. In order to evaluate its applicability in the actual world, testing is done with a selected portion of data that was not utilized during learning.

Gear Up for your Next Machine Learning Interview!

It may be just the beginning of an agricultural revolution with the introduction of agriculture machine learning projects. Applications of AI and ML keep getting more affordable and readily available as technology develops. Machine learning in agriculture could potentially be an essential component towards securing our future food supplies in the midst of changing environmental conditions and an increasing global population. 

With the need for better technology, job opportunities in the ML industry are increasing every day. Interview Kickstart has designed the perfect program for every machine learning and agriculture enthusiast, wherein you can learn various ML techniques. This program would help you easily land an excellent job with the tech giants. Sign up now for the free webinar today!

FAQs on Machine Learning in Agriculture

  1. What type of AI is used in agriculture?

AI-powered tools, such as drones with image recognition tools and sensors, are being extensively used in agriculture for tracking any changes in crops, soil management, insect detection, etc.

  1. Which countries are using AI in agriculture?

The countries using AI technology in agriculture are India, France, USA and Iran.

  1. What are the three major domains of AI?

The major domains of AI are machine learning, natural language processing, computer vision, data science and deep learning.

  1. What device is used for smart farming?

The devices used for smart farming are robots, computer imaging, remote sensors and drones.

  1. What are the potential disadvantages of using AI in agriculture?

Some of the disadvantages of using AI in agriculture are as follows: 

  • High energy cost and maintenance
  • Less accessibility to small farmers
  • Increased cost and time for research 

Posted on 
October 6, 2023

Abhinav Rawat

Product Manager @ Interview Kickstart | Ex-upGrad | BITS Pilani. Working with hiring managers from top companies like Meta, Apple, Google, Amazon etc to build structured interview process BootCamps across domains

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