(ready)BGATU-Kafedra-prakticheskoj-lingvistiki-NIRS-2026

1.

БГАТУ · Кафедра практической
лингвистики · НИРС 2026
The Role of Neural
Networks in
Optimizing
Agricultural
Production
Таланков Тигран Фаридович · Студент 23а, АЭФ

2.

THE BIG IDEA
Why Neural Networks Matter in Agriculture
Agriculture faces unprecedented challenges: a
projected global population of 9.7 billion by
2050, erratic weather patterns, shrinking arable
land, and an urgent need to reduce
environmental footprint.
For decades the industry relied on "blanket"
applications of resources—spraying entire fields
regardless of localized needs. Neural networks
now enable a fundamental shift:
From "average field management" to
"individual plant care."
Unlike traditional software, neural networks
learn from historical data, recognize complex
patterns, and adapt to the unique biological
variables of a specific farm.

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Deep Learning Architectures in Agriculture
Three Core Neural Network Types
CNNs
Image recognition for weeds
and diseases; cuts scouting
by 60%
RNNs & LSTM
Time-series forecasting for
soil moisture and weather;
enables preemptive irrigation
GANs
Generate synthetic training
data to augment rare disease
datasets

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Main Directions of Production Optimization
Computer Vision: Monitoring & Diagnostics
Weed recognition: Neural networks distinguish cultivated plants from weeds, triggering spot herbicide application only where needed — reducing chemical usage by up to 90%.
Disease diagnostics: Early detection of powdery mildew, late blight, and other diseases by analyzing leaf color and shape changes, enabling localized outbreak control.
Seedling counting: Automatic counting of plants per square meter to assess density and predict future yield.
Predictive Analytics & Yield Forecasting
Risk management: Agricultural holdings identify which fields will suffer losses in dry years, adjusting planting strategies in advance.
Insurance: Insurance companies use neural network data to calculate objective crop insurance rates.
Logistics planning: Knowing approximate harvest volumes, companies can contract machinery, storage, and transportation ahead of time.
Robotization & Autonomous Machinery
Neural networks serve as the "brain" of autonomous agricultural machinery. Tractors and combines equipped with cameras and lidars recognize obstacles, field boundaries, and already-treated areas.
This enables 24/7 planting and plowing with maximum precision — eliminating human error, operator fatigue, and addressing the critical shortage of rural labor.

5.

What the Data Shows
Practical Case Studies
See & Spray (USA): Blue River
Technology (acquired by John Deere)
scans soil at 20 frames per second,
opening nozzles only when a weed is
detected — saving up to 80% on
herbicides.
Soybean Forecasting (Brazil):
University of Campinas researchers
achieved 92% forecast accuracy two
months before harvest using MODIS
satellite imagery and vegetation
indices.
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Wheat Disease App (Russia): A mobile
application allowing agronomists to
photograph a plant and receive an
instant diagnosis with 95% accuracy for
major diseases.
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Key Terminology & Technology Stack
Core AI Concepts
Agricultural Applications
Neural Networks (Нейронные сети)
Machine Learning (Машинное обучение)
Deep Learning — CNNs, RNNs, LSTMs, GANs
Computer Vision (Компьютерное зрение)
Generative Adversarial Networks
Precision Agriculture (Точное земледелие)
Yield Prediction (Прогнозирование урожайности)
Production Automation (Автоматизация)
AgTech (Агротехнологии)
Resource Optimization (Оптимизация ресурсов)
Data & Sensing Technologies
Future Concepts
Remote Sensing (Дистанционное зондирование)
Internet of Things (Интернет вещей)
UAVs / Drones — 20 fps image scanning
LiDAR sensors for obstacle recognition
MODIS satellite imagery
Digital Twins of fields (виртуальные модели)
Sustainable Development (Устойчивое развитие)
Cloud-based processing centers
PAR-optimized grow systems
Interdisciplinary Data Science + Agronomy

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PROBLEM SOLVING
Challenges & Development Prospects
Challenge: Implementation Cost
Equipment (drones, sensors, lidars) and
specialized software require significant
investment, often beyond the reach of
small farms. This creates a competitive
gap between large agro-holdings and
family farms.
Challenge: Data Quality
Many farms still keep records in paper
journals. Digitizing archival data is a
labor-intensive process that creates a
significant barrier to deploying effective
machine learning models.
Challenge: Digital Divide
Rural areas often lack high-quality
4G/5G connectivity, which is necessary
for transmitting large amounts of data
from field machinery to cloud processing
centers in real time.
Challenge: Shortage of Skilled
Personnel
Maintaining neural networks requires
interdisciplinary specialists —
agronomists who also possess Data
Science skills. This rare combination is
currently scarce in the agricultural
sector.
Future Prospect — Digital Twins: A virtual copy of agricultural land where neural networks model all processes
from spring snowmelt to grain ripening, allowing agronomists to select the ideal strategy for the real field before
acting.

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Conclusion
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Unprecedented Precision
Reduced Uncertainty
Computer vision enables the transition from "blanket" to
"targeted" application of fertilizers and crop protection
products, yielding powerful ecological and economic
effects.
The ability to forecast yields, diseases, or water shortages
allows agribusinesses to act proactively — minimizing
losses and locking in favorable contracts in advance.
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Automation Addresses Labor Gaps
A Paradigm Shift, Not Just a Tool
AI-based robotization resolves rural labor shortages while
simultaneously increasing productivity and the quality of
agronomic operations.
Success requires a comprehensive approach: training
interdisciplinary personnel, building rural communications
infrastructure, and creating accessible tools for small
businesses.
Neural networks will not replace the agronomist — but will equip them with an enormously powerful tool. Investments in
these technologies today are investments in food security and sustainable development tomorrow.
References: John Deere See & Spray™ (2024) · DOS Group AI in AIC Report (2025) · Petrova E.A., UAVs in Agriculture (2024) · FAO, State of
Food and Agriculture (2024)

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Thank you for attention:)