Agriculture and farming are some of the oldest and most important professions in the world. It plays an important role in the economic sector; especially in India, where agriculture has been the primary occupation for ages. The global population is expected to reach more than nine billion by 2050 which will require an increase in agricultural production by 70% to fulfill the demand. In this article, guest author Melanie writes about the applications of AI and Machine Learning in agriculture.
Applications of AI and Machine Learning in Agriculture
By Melanie Johnson
The success of a business or industry is dependent on several factors, one of which is effective decisions. Narrowing this down to agriculture, there is an increasing number of things that affect decision-making, including climate change, crop specifications, and soil conditions – all requiring farmers to prioritize one thing over the others on a daily basis.
Thanks to artificial intelligence (AI) and machine learning (ML), farmers can now access advanced data and analytics tools that will foster better farming, improve efficiencies, reduce waste in biofuel and food production while at the same time minimizing the negative impact on the environment.
Worldwide, agriculture is a $5 trillion industry. According to Forbes, Agriculture Is One Of The Most Fertile Industries There Are For AI & Machine Learning. Spending on AI technologies and solutions alone in Agriculture is predicted to grow from $1 billion in 2020 to $4 billion in 2026, attaining a Compound Annual Growth Rate (CAGR) of 25.5%, according to Markets&Markets.
This article seeks to explain various applications of machine learning in the agriculture industry and their overall impact in revolutionizing farming. Here are the four major categories we’ll cover:
- Crop management
- Livestock management
- Field conditions management
- Species management
Crop Management
Sophisticated ML approaches help farmers predict harvest yields, detect crop diseases, evaluate crop quality, and identify plant species:
● Yield Prediction
When predicting harvest yields, matching crop supply with the market demand and crop management to boost productivity becomes critical. ML-powered approaches such as the support vector machine (SVM), a supervised ML model used in rice farming, are being applied in yield mapping and estimation.
● Disease Detection
Farmers normally spray pesticides over the cropping area, whether in open-air or greenhouse settings, for better production. Alternatively, farmers can now use ML as part of precision agriculture management, where agrichemicals input is applied based on time, place, and affected crops.
● Crop Quality Evaluation
For product prices to go up and reduce waste, farmers need to detect and classify crop quality features accurately. Machines can use data to detect and reveal new characteristics that contribute significantly to the overall crop quality.
● Plant Species Identification
Machines automate the identification and classification of plant species, effectually reducing classification time.
Livestock Management
Livestock management involves general animal welfare and livestock production. ML technology has improved livestock farming in various areas, including dairy production, animal health maintenance, and herding, and selective breeding.
● Animal Welfare
The general animal welfare looks at animal behavior and disease detection. ML technologies such as depth video cameras trace and monitor various animal activities around the farm. ML-powered collar, halter, and ear tag sensors can also collect data that can be used to analyze animal behavior.
● Livestock Production
Just like crop management, ML gives accurate prediction and estimation of farming parameters useful in optimizing the production of milk, meat, eggs, and other dairy products. For instance, a weight predicting system can estimate the future weight of a bull 5 months prior to the slaughter day, giving the farmer the opportunity to adjust the bull’s diet and living conditions as needed.
Field Conditions Management
● Soil Management
Agriculture experts seek to understand the complexity of soil properties as a natural resource. It is through ML algorithms that soil dynamics such as soil moisture, evaporation processes, and temperature can be studied and understood.
● Water Management
Water management in agriculture has a significant impact on the agronomical, climatological, and hydrological balance. ML-based applications are capable of estimating daily, weekly, or monthly evapotranspiration, eventually leading to the effective utilization of irrigation systems. Moreover, the accurate prediction of daily dew point temperature assists in the identification of expected weather phenomena, and also the estimation of evapotranspiration and evaporation.
Species Management
● Species Selection
Species selection is a long process that involves looking for specific genes that determine how effective water and nutrient usage is, how adaptable to climate change they are, and how well they can resist diseases.
With ML, farmers can analyze big chunks of field data to understand crop performance over the years under different conditions and new characteristics formed in the process. From the data analyzed, it becomes easier to develop a probability model to predict which genes have high chances of contributing a beneficial feature to a plant.
● Species Recognition
The traditional method for classifying plants is comparing leaf color and shape. However, ML introduces a more accurate and quicker way of classification by analyzing leaf vein morphology that has more information about the leaf properties, sometimes even using aerial imagery.
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Anticipated Challenges for the Agriculture Industry by 2050
There are two major challenges the agriculture industry is expected to face by 2050:
- According to a recent report from the United Nations, the world population will reach 9.7 billion by 2050 (currently 7.7 billion). That means the global food system will be strained and expected to provide food to the extra 2 billion people.
- By 2050, it will be difficult to increase harvest yields due to climatic factors such as global warming, increased urbanization, and water shortages. With global warming, negative issues like increased heatwaves, rising sea levels, and changing pests and diseases will worsen.
ML is the antidote to these challenges. Agriculture experts need to rethink the existing agricultural systems and be ready to apply ML to address them. For example, ML is proven capable of boosting agricultural productivity while at the same time keeping the impact on the environment at a minimum. ML will ultimately help humanity achieve food security.
Conclusion
Farmers are using AI and ML models to boost productivity more often, and so far, the food-tech segment is the biggest beneficiary of these innovations. Technologies such as robots and sensors are currently being used to manage and monitor crops, as well as collect data related to crops. That said, there is a rising opportunity in ML for application in digital agriculture.
ML is a secure way of maximizing agricultural productivity at the same time minimizing the impact on the environment. Through data collected from crops, farmers are able to understand crops, their genes, and potential diseases better. This data will help farmers make quick, informed, and results-driven decisions. Lastly, as the world population grows, ML is the solution to addressing the issue of food security and scarcity to meet the rising demand in the global food system.
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About Melanie:
Melanie Johnson is an AI and computer vision enthusiast with a wealth of experience in technical writing.
She is passionate about innovation and AI-powered solutions and loves sharing expert insights and educating individuals on tech.
Featured Image Source: Analytics Vidhya