Artificial Intelligence in Agriculture is transforming traditional decision-making into accurate, data-driven systems using Machine Learning, Computer Vision, and IoT.
These technologies enable crop yield prediction, plant disease detection, and resource utilization optimization.
In this article, we explore the technical applications of AI in agriculture, focusing on system architectures and practical examples.
The goal is to provide an engineering perspective for AgriTech programmers and developers.
What is AI in Agriculture and how does it work?
AI in Agriculture is the use of ML, CV, and IoT to make data-driven decisions on the farm.
Data is collected from sensors, drone imagery, and crop history.
Machine learning models transform this data into actionable predictions and recommendations.
This architecture is the foundation of all AgriTech applications.
Monitoring and diagnosis (with computer vision and IoT):
Using satellite imagery, drones, and ground sensors to monitor crop health, detect pests and diseases, and detect water and nutrient deficiencies.
Example: Algorithms can detect symptoms of fungal disease from images earlier than the human eye.
Precision farming:
Analyzing soil, weather, and climate data to more accurately apply inputs (such as water, fertilizer, pesticides).
Example: Self-driving tractors with AI maps distribute seeds and fertilizers specifically to each point in the field.
Forecasting and analytics (with machine learning):
Predicting crop yield based on historical and weather data.
Predicting market prices and managing the supply chain.
Example: Predictive models can determine the best time to harvest.
Robotics and automation:
Harvesting robots (such as harvesting tomatoes or strawberries).
Weeding robots that accurately detect and remove weeds.
Livestock management:
Monitor livestock health with cameras and sensors (disease detection, nutrition and birth monitoring).
Benefits:
Increases efficiency and reduces costs
Reduces water, fertilizer and pesticide use (more sustainable agriculture)
Contributes to global food security
Reduces dependence on seasonal labor
Challenges:
High initial cost
Requires internet and data infrastructure
Requires farmer training
Data privacy issues
Overall, AI is transforming agriculture from a traditional activity to a data-driven and intelligent industry that can help solve challenges such as climate change and population growth.
Application of Machine Learning in Crop Yield Prediction
Machine Learning predicts crop yield by analyzing historical and environmental data.
Regression and Random Forest models are trained on soil and climate data.
In real projects, the forecast error has been reduced to less than 10%.
This forecast is the basis for financial and logistical planning.
How does ML predict crop yield?
This process has several key steps:
Data Acquisition:
ML models require a large amount of data. This data is collected from various sources:
Satellite and drone data: multispectral and hyperspectral images that calculate vegetation indices (such as NDVI).
Meteorological data: temperature, precipitation, humidity, sunshine hours, etc.
Soil data: soil texture, soil moisture, nitrogen, phosphorus, potassium, and pH.
Management data: planting date, variety type (seed), water, fertilizer, and pesticide application rates.
Historical data: crop yield in previous years for a specific field or region.
Data Processing and Integration:
Raw data is cleaned, homogenized, and integrated into a dataset. This step is critical for the quality of the prediction.
Model Selection and Training:
Depending on the type of data and the problem, different ML algorithms are used. Some of the most common are:
Regression: To predict a numerical value (e.g. tons per hectare).
Linear, tree, and random forest regression.
Deep Neural Networks: To analyze complex satellite imagery and find nonlinear patterns.
Ensemble Machine Learning Methods: Such as XGBoost or LightGBM, which are a combination of multiple models and often provide the best accuracy.
Time Series Models: To analyze trends over time.
The model is trained on historical data (“training data”) to learn the relationship between the features (independent variables such as rainfall and NDVI) and the target (dependent variable i.e. yield).
Evaluation & Prediction:
The trained model is tested and validated on new data (e.g. current season data). If it is accurate enough, it can be used to predict yield in the near future or at the end of the season.
Practical Applications and Benefits:
Input Optimization: Predicting low-yield areas of the field helps the farmer to use water, fertilizer and pesticides more accurately and in a targeted manner.
Risk Management and Insurance: Farmers and insurance companies can quantify the risk of yield reduction based on climate forecasts.
Logistics and Market Planning: Forecasting the total production of a region helps in planning for storage, transportation and market price regulation.
Crop and variety selection: The model can suggest which crop or seed variety will perform better in a specific region, given the weather forecast.
Early forecasting: Ability to predict yield several months before harvest, which is very valuable for strategic decision-making.
Concrete example:
An ML model could work like this:
Inputs (features): Average summer temperature + total spring precipitation + soil nitrogen content + NDVI index at flowering stage (from satellite imagery).
Output (target): Predicted wheat yield (in tons per hectare).
Challenges and limitations:
Data quality and quantity: The model requires a lot of accurate and historical data, which is not available in many regions.
Complexity of biological systems: Unpredictable factors such as sudden pest attacks or hail can disrupt the prediction.
Scale: Sometimes the model works well for a large area, but is less accurate for a specific farm (and vice versa).
Accessibility and cost: Advanced technologies such as satellites and drones may not be cost-effective for smallholder farmers.
As a result, ML has dramatically increased the accuracy of yield predictions by transforming agriculture from a guesswork-based activity to a quantitative, data-driven process, helping farmers and policymakers make better decisions. This technology plays a key role in food security and sustainable agriculture.
The role of machine learning in planting and harvesting scheduling
ML determines the best planting and harvesting times with time series models.
Algorithms such as LSTM analyze temperature and precipitation changes.
In wheat fields, this method has increased yields by up to 15%.
Accurate scheduling reduces climate risk.
By analyzing complex environmental, historical, and agronomic data, machine learning (ML) transforms planting and harvesting scheduling from an empirical decision to a scientifically optimized process. This is done by more accurately predicting future conditions and identifying hidden patterns.
To view the rest of the article, visit the [Striing.ir] website (https://www.striing.ir/article/142:ai-in-agriculture-machine-learning-computer-vision-iot).
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