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.
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
Chief Operating Officer and Business Head at nurture.farm
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
Let's look at the most potent applications in agriculture for crop, soil, water, and livestock management and control.
Crop management
Water management
Soil management
Livestock management
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.
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.
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.
Machine learning helps farmers optimize their irrigation schedules, fertilizer application rates, and pesticide use to reduce wastage and environmental harm.
ML automates field mapping, monitoring crop health, and applying fertilizers. This can save farmers time and money and reduce their need for hired labor.
ML helps farmers to optimize resources, resulting in increased crop yields. This can help to improve food security and reduce hunger.
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.
Machine learning enables farmers to make better decisions about when to plant, how to irrigate, and when to apply fertilizers.
ML helps farmers avoid hazardous tasks such as working with pesticides. This can improve farm workers’ safety and health.
ML can provide farmers with personalized recommendations for planting, irrigation, and fertilization.
ML helps farmers adapt their practices to cope with changing weather patterns, which can build resilience to climate change.
ML helps farmers manage their land in a way that conserves biodiversity. This can safeguard ecosystem services and preserve natural resources.
ML helps farmers produce food that is safer and of higher quality. This can improve public health and increase profitability for farmers.
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.
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.