Agriculture is a pillar of civilization, essential to feeding the world’s growing population. However, climate change, water scarcity, land degradation, and other pressures pose immense challenges to global food security. Advanced technologies such as artificial intelligence (AI) and machine learning offer new opportunities to improve agricultural productivity, efficiency, and sustainability. In this article, we define machine learning and its subset, deep learning, review applications of Machine learning in agriculture, and discuss research frontiers. We aim to provide a technical overview of how machine learning can address agricultural challenges to support food security.

Machine Learning’s Unprecedented Capabilities

Before delving into the applications, it’s crucial to comprehend why machine learning is a game-changer in agriculture. Unlike traditional methods that often rely on guesswork and intuition, machine learning operates on data-driven insights. By analyzing historical and real-time data, machine learning algorithms can identify patterns, correlations, and anomalies that would be humanly impossible to detect.

Why machine learning in agriculture is important?

The agricultural industry is facing many risks and uncertainties due to the change in climatic conditions and market trends, which cause significant losses in production and waste of resources. In recent decades, experience combined with accurate weather data has helped farmers make accurate guesses in the field of agriculture, but more success and diversity can still be achieved in this field.

Benefits of machine learning in agriculture

Machine learning in agriculture creates advantages that being aware of can help a lot to people working in this field.

• Reducing environmental risks: Machine learning helps farmers to optimize irrigation schedules, fertilizer use and pesticide use to reduce waste and environmental damage.
• Save time and labor: Machine learning automates field mapping, crop health monitoring, and fertilizer application. This issue can save farmers time and money and reduce their need for hired labor.
• Improving yield: Machine learning helps farmers to optimize resources, resulting in increased crop yields, which can help improve food security and reduce hunger.
• Cost reduction: Machine learning helps farmers save on vital resources such as water, fertilizers, and pesticides, which can increase profitability and make agriculture more sustainable in the long run.
• Improved decision-making: Machine learning in agriculture gives farmers the ability to make better decisions about when to plant, how to irrigate, and when to use fertilizer.
• Increased safety: Machine learning helps farmers avoid dangerous tasks such as working with tractors. This can improve the safety and health of farm workers.
• Providing personalized advice: Machine learning can provide personalized recommendations for planting, watering and fertilizing to farmers.
• Building resilience to climate change: Machine learning in agriculture helps farmers adapt their practices to cope with weather patterns that can create resilience to climate change.
• Biodiversity Conservation: Machine learning helps farmers manage their land in a way that preserves biodiversity. This can protect ecosystem services and conserve natural resources.
• Improving food quality: Machine learning helps farmers produce safer and better-quality food. This can improve public health and increase profitability for farmers.

Importance of Agriculture and Its Challenges

Agriculture provides the fundamental biological sustenance underpinning human societies. Key challenges in coming decades include:

• Feeding 10 billion people: The global population will approach 10 billion by 2050, requiring boosting food production by an estimated 50%. More output must be sustainably achieved from the same or even diminished resources.
•  Climate change stresses: Rising temperatures, changing precipitation patterns, and more extreme events will pressure crop yields and livestock health. Adaptation and resilience to maintain production is imperative.
•  Environmental degradation: Soil erosion, aquifer depletion, biodiversity decline, and agricultural runoff damage ecosystems but must be curbed to ensure long-term viability.
•  Changing food preferences: Rising incomes drive greater consumption of resource-intensive meat, dairy, and processed foods, increasing agricultural burdens.

Satisfying global nutrition needs in coming decades under these intensifying constraints presents a monumental challenge requiring transformational innovation. Advanced technologies like AI and machine learning offer promising capabilities.

Machine Learning vs. Artificial Intelligence in Agriculture

Machine learning, which falls under the umbrella of artificial intelligence, focuses on algorithms that derive insights from data, allowing them to formulate predictions or decisions without explicit programming instructions. The key distinction is that machine learning emphasizes automated, data-driven learning, while AI can encompass broader capabilities, including human-coded rules and logic. However, machine learning underpins many applied AI solutions because it allows systems to continuously refine themselves based on new agricultural data patterns. So, while AI may use machine learning, it also involves knowledge engineering, which requires human expertise.

Read more: AI in Agriculture | Practical Application & Future of AI in Agriculture

How Machine Learning Can Be Used in Agriculture?

Machine learning in agriculture offers diverse capabilities to address agricultural challenges:

Crop and soil modeling

Simulate and predict crop development and yields based on variables like genetics, management practices, weather, and soil conditions using supervised learning regression techniques. Allows optimizing interventions.

Disease/pest detection

Classify plant diseases and insect pests from imagery using convolutional neural networks for early intervention and targeted spraying.

Weed/crop segmentation

Detect locations of unwanted weed species amidst crops using pixel-wise semantic segmentation models on UAV imagery to guide selective treatment.

Water management

Apply reinforcement learning to dynamically control irrigation to maximize crop yield versus water use efficiency.

Livestock monitoring

Track animal movement, behaviour, and health with computer vision, wearables, and anomaly detection algorithms. Enables early disease diagnosis.

Agrichemical optimization

Tune precision variable rate applications of fertilizers, pesticides, and herbicides across fields using ML models integrating soil, weather, and crop data. Minimizes ecological impacts. Machine learning provides both predictive insights to guide decisions as well as operational capabilities assisting farmers in applying interventions.

Yield prediction

The process of predicting returns is essential for the global and regional economy. Knowing when to harvest and what crops to grow to meet market needs is important for any farm. Many parameters can affect the yield quantity, including environmental factors, phenotypic data and weather information. Modern machine learning models can analyze all factors to increase the accuracy of yield prediction.

Selective breeding

Traditional crop breeding follows the principle of Mendelian inheritance and states that one gene controls each trait. However, more recent research has shown that most traits are controlled by multiple genes that interact with each other and the environment in complex ways. This shows that traditional breeding methods are usually inefficient and can take several generation and time to get the desired results. On the other hand, artificial intelligence can identify patterns in data that may not be obvious to humans, so it can quickly and more efficiently identify desired traits for modification.

Agricultural Knowledge Mining

There is a wealth of agricultural knowledge and insights contained in large volumes of unstructured data like research papers, government reports, articles, manuals, and documents. But extracting useful information from these text sources has been difficult. Recent advances in natural language processing and text mining using machine learning now enable systematically mining such corpora to discover hidden patterns and actionable knowledge:

• Topic modeling algorithms can rapidly parse thousands of agricultural documents to extract frequently discussed concepts and trends. This allows aggregating knowledge on specific issues like pest management or irrigation practices.
• Text classifiers developed on labelled text can categorize agricultural documents into domains like crop science, horticulture, animal husbandry etc. This enables filtering sources by relevance.
• Entity recognition techniques can identify key objects like crop varieties, fertilizers, pests etc. and their relationships in unstructured text. This automates knowledge graph construction.
• Sentiment analysis of text can discern farmer reactions and opinions on agricultural technologies and policies guiding R&D.
• Summarization algorithms can distill lengthy reports into concise insights farmers can readily absorb.

Overall, text mining using machine learning, combined with knowledge engineering, helps unlock the knowledge trapped in agricultural literature to augment farmer skills.

Deep Learning in Agriculture

A subset of machine learning seeing massive agricultural investment is deep learning. Deep neural networks composed of many layers discover layer-wise data representations. Key applications include:

Computer vision

Deep convolutional neural networks (CNNs) accurately analyze imagery for plant identification, disease spotting, and weed mapping tasks.

Time series analytics

Recurrent neural networks (RNNs) reveal insights from temporal data like weather records, crop growth curves, and commodity prices.

Genomics

Deep learning extracts meaningful patterns from vast DNA/RNA sequencing data to support molecular breeding and microbiome manipulation.

Language processing

Techniques such as Long-Short Term Memory (LSTM) networks analyze masses of unstructured agricultural text and spoken dialog for search, translation, and decision support. Deep learning overcomes the limitations of earlier machine learning, which relied on hand-crafted data inputs and simplified models that were unable to harness agriculture’s massive amounts of structured (imagery, genomics) and unstructured (language, sensors) data.

   

Applications Of Machine Learning in Agriculture

We will overview several real-world examples that demonstrate the transformative potential of machine learning in agriculture:

Reimagining Crop Management

One of the most important applications of machine learning in agriculture is crop management. Predicting crop yields, disease outbreaks, and optimal planting times is critical to efficient farming. Machine learning models can incorporate a variety of factors (weather patterns, soil conditions, historical yield data, and even satellite imagery) to make accurate predictions. Armed with this knowledge, farmers can make strategic decisions that optimize crop production while minimizing the waste of resources.

Precision Agriculture

Precision agriculture is a trendy term, and machine learning is at its core. This approach involves tailoring farming practices to the unique needs of different sections of a field. Machine learning algorithms process data from IoT devices, drones, and sensors to create detailed maps of soil characteristics, moisture levels, and crop health. These insights enable farmers to apply fertilizer, irrigation, and pesticides precisely where they are needed, reducing environmental impact and optimizing resource use.

Detecting and Preventing Crop Diseases

The ability to detect crop diseases early is critical to preventing their spread and minimizing economic losses. Machine learning in agriculture excels at image recognition, enabling the development of models that can distinguish between healthy and diseased crops based on images captured by drones or smartphones. These models can provide real-time insights to farmers, allowing them to take quick action and implement targeted treatment plans.

Supply Chain Optimization

Agriculture doesn’t conclude at the farm gate; it extends to the entire supply chain. Machine learning algorithms can enhance logistics and distribution by predicting demand, optimizing routes, and minimizing food wastage by considering factors such as consumer preferences, market trends, and transportation constraints; machine learning-driven supply chain management ensures that produce reaches its destination in optimal condition and quantity.

     

 

 

Conclusion

Advanced deep neural networks are unlocking value from vast new data sources. Machine learning in agriculture offers immense potential for addressing rising pressures on agricultural systems essential to global food security and ecological sustainability. Real-world implementations are proving capabilities to increase yields, reduce costs and environmental impacts, and build resilience.