Machine learning in agriculture: use
cases and applications

Machine learning in agriculture: use cases and applications

February 1, 2023

Machine learning is a growing field with many potential applications in agriculture. Farmers and agricultural scientists are exploring how turning to machine learning development can improve crop yields, reduce water usage, and predict pests and diseases. In the future, machine learning may help farmers to use resources more efficiently and produce food sustainably.

Machine learning in agriculture: a summary
Machine learning in agriculture allows farmers to use lavish amounts of data about climate change, crop and soil conditions, and other environmental variables to make informed decisions about plant and animal treatment.
Let's discuss the 13 most potent use cases of machine learning in agriculture, its benefits, real-life examples, applications, and the future outlook.

Why machine learning in agriculture is important

The farming sector faces multiple risks and uncertainties due to changing climatic conditions and market trends, which results in significant production losses and wasted resources. While decades of experience coupled with ever-more precise weather data have helped farmers make educated guesses, there is still too much success variability.

Smallholder farmers own 470 out of 570 million farms worldwide. They don't have the requisite capital, lack the skills to use technology, or are unaware of the solutions available to help them farm better and drive profitability. More than 70% of farmers worldwide lack access to proper capital, two-thirds of them struggle to use technology and more than 50% are not aware of the existing solutions. We need to work together to educate, train and scale our efforts to deliver the benefits of digitalisation, AI, farm mechanization, and ML to farmers.

Dhruv Sawhney

Dhruv Sawhney

Chief Operating Officer and Business Head at nurture.farm

ML in agriculture: market stats

Estimated CAGR of the ML in agriculture market from 2022 to 2030

Emergen Research

Annual cost of plant diseases to the global economy

FAO

Market value of the IoT-enabled agricultural (IoTAg) monitoring by 2025

PwC

Chart title: Smart agriculture market by value, 2018-2028

Data source: BlueWeave Consulting

13 ML use cases in agriculture

Let's look at the most potent applications in agriculture for crop, soil, water, and livestock management and control.

ML in agriculture

Crop management

  • Yield prediction
  • Disease detection
  • Weed detection
  • Crop recognition
  • Grading by quality
  • Selective breeding

Water management

  • Irrigation
  • Soil water monitoring
  • Leak detection
  • Weather monitoring
  • Weather prediction
  • Water usage prediction

Soil management

  • Pesticides and fertilizers
  • Fertility prediction
  • Soil sensitivity
  • Moisture prediction
  • Organic carbon prediction
  • Insect detection

Livestock management

  • Animal welfare monitoring
  • Precision livestock farming
  • Monitoring production quality
  • Monitoring living conditions
  • Grazing control
  • Disease detection

1 Weed detection

Weeds are a notorious enemy of every farmer. They quickly grow, take over crop territories, cause numerous plant diseases, and lower yield. The most widespread way of dealing with weeds is herbicides. While this method is effective, farmers usually shower entire fields with herbicides, causing significant harm to the environment. Computer vision-powered solutions can identify unwanted plants, allowing farmers to apply herbicides to selected areas rather than the whole field. This can drastically decrease the harmful effects of herbicides.

2 Disease detection

Crop diseases are one of the main threats in agriculture that drastically decreases yield quality and quantity. Traditionally, agronomists manually inspect fields and look for signs of crop diseases like curled leaves or wilting, which is time-consuming and error-prone. ML-based image software analysis tools can assess the health of the soil and individual crops, limiting the application of pesticides only to sick plants instead of the whole field.

3 Yield prediction

Yield prediction is critical to the economy both in a global and regional sense. Knowledge of when it is best to harvest the crop and what crops to grow to satisfy market demands is crucial for any farm. Many parameters can influence yield quantity, including environmental factors, phenotypic data, and weather information. Modern ML models can analyze all the factors to increase yield prediction accuracy.

4 Selective breeding

Traditional crop breeding embraces the principle of Mendelian inheritance, stating that one gene controls each trait. However, more recent research has shown that most traits are controlled by multiple genes, which interact with each other and the environment in complex ways. This means that traditional crop breeding methods are often inefficient and can take many generations to produce the desired results. Artificial intelligence, on the other hand, can identify patterns in data that may not be apparent to humans. Therefore, it can more quickly and effectively identify desired traits for breeding.

Examples of ML in agriculture

Trace Genomics is a California-based startup focused on ML-enabled soil analysis. Instead of healing already damaged crops, Trace Genomics decided to get to the root cause of the problem and prevent crop nutrient deficiencies and common diseases by ensuring appropriate soil conditions. Large agricultural enterprises, small farmers, and everyone in between can send soil samples to Trace Genomics and receive a complete overview of soil conditions and actionable insights on soil management.

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Machine learning in agriculture

Predictive analytics

A subset of data science, predictive analytics technology can be used in agriculture to help farmers better predict crop yields, forecast demand for specific crops, and optimize irrigation and fertilizer usage. By analyzing past data patterns, predictive analytics can provide insights that can help farmers make more informed decisions about when to plant, how to care for their crops, and what prices to charge for their produce. In addition, predictive analytics can identify early warning signs of crop pests and diseases, allowing farmers to take preventive measures and avoid or mitigate potential damage.

Other game-changing technologies in agriculture

Internet of Things
IoT plays a critical role in machine learning for agriculture, as it allows us to measure important factors, including soil moisture, air temperature, and crop health. This data can help farmers identify patterns and make predictions to optimize operations.
Drones
By using drones, farmers can quickly gather data about their crops, including information on plant health, water levels, and nutrient levels. Additionally, drones can help apply pesticides and herbicides, saving time and money compared to traditional methods.
Chatbots
Chatbots are commonly used to automate customer service or sales tasks. However, chatbots can be a convenient interface for accessing information about crop or livestock conditions. Chatbots can also advise farmers on irrigation and fertilization and assist in financial planning and management.
Robots
Agricultural robots are used in smart agriculture to help with tasks such as crop monitoring, planting, and harvesting. They can also be used to apply pesticides and herbicides and to water plants. Agricultural robots can be controlled remotely, or they can be autonomous.

Machine learning models used in agriculture

Regression

Regression models can predict crop yields, the price of agricultural commodities, and the demand for agricultural products. In addition, regression models can be used to study the impact of new technologies on agriculture and to evaluate the efficiency of different agricultural production planning systems.

Clustering

Clustering models can be used in agriculture to group plants with similar characteristics. This can be useful for identifying which plants are more likely to thrive in certain conditions and developing targeted interventions.

Bayesian models

Bayesian models can provide accurate crop yield predictions by using data on weather, soil conditions, and other factors. Farmers can use this data to decide what crops to plant, how much fertilizer to use, and when to harvest.

Artificial neural networks

Neural networks are well-suited for agricultural applications because they can learn to identify patterns in data that are too complex for humans to discern. For example, artificial neural networks can be used to develop new strains of crops that are more resistant to pests or diseases.

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Benefits of machine learning for agriculture

Mitigating environmental risks

Machine learning helps farmers optimize their irrigation schedules, fertilizer application rates, and pesticide use to reduce wastage and environmental harm.

Saving time and labor

ML automates field mapping, monitoring crop health, and applying fertilizers. This can save farmers time and money and reduce their need for hired labor.

Improving yields

ML helps farmers to optimize resources, resulting in increased crop yields. This can help to improve food security and reduce hunger.

Reducing costs

ML helps farmers save money on crucial resources like water, fertilizer, and pesticides. This can increase profitability and make farming more sustainable in the long term.

Improving decision making

Machine learning enables farmers to make better decisions about when to plant, how to irrigate, and when to apply fertilizers.

Enhancing safety

ML helps farmers avoid hazardous tasks such as working with pesticides. This can improve farm workers’ safety and health.

Providing personalized advice

ML can provide farmers with personalized recommendations for planting, irrigation, and fertilization.

Building resilience to climate change

ML helps farmers adapt their practices to cope with changing weather patterns, which can build resilience to climate change.

Protecting biodiversity

ML helps farmers manage their land in a way that conserves biodiversity. This can safeguard ecosystem services and preserve natural resources.

Improving food quality

ML helps farmers produce food that is safer and of higher quality. This can improve public health and increase profitability for farmers.

Solving key agricultural challenges using ML techniques

Challenge

Solution

Low yields

Machine learning can help farmers identify optimal planting and irrigation schedules, as well as predict ideal conditions for crop growth.

Pest and disease outbreaks

Machine learning is used in early warning systems that alert farmers about potential outbreaks. It can also be used to develop models for predicting the spread of pests and diseases.

Soil degradation

Machine learning can help farmers identify areas of degradation and map out management plans to improve soil health.

Water scarcity

Machine learning can help farmers optimize irrigation schedules and identify alternative water sources.

Climate change

Machine learning can help farmers adapt to changing conditions by identifying optimal growing conditions and developing early warning systems for extreme weather events.

 Stay ahead of the competition with ML

Stay ahead of the competition with ML

Artificial intelligence has the potential to significantly improve the agricultural industry by reducing environmental harm, improving yields and food quality, and making processes more efficient. Implementing machine learning in agricultural processes is crucial for staying ahead of the competition. Those who can adopt these technologies will be well-positioned to reap the benefits. At Itransition, we are committed to helping our clients stay at the forefront of innovation by providing expertise in cutting-edge technologies like machine learning and IoT. Contact us today to learn how we can help you improve your agricultural operations.

 Stay ahead of the competition with ML

FAQ

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