February 1, 2023
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.
Estimated CAGR of the ML in agriculture market from 2022 to 2030
Annual cost of plant diseases to the global economy
Market value of the IoT-enabled agricultural (IoTAg) monitoring by 2025
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.
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.
Artificial neural networks
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.
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.
Machine learning can help farmers identify areas of degradation and map out management plans to improve soil health.
Machine learning can help farmers optimize irrigation schedules and identify alternative water sources.
Machine learning can help farmers adapt to changing conditions by identifying optimal growing conditions and developing early warning systems for extreme weather events.
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