Agriculture and agri-food are strategic sectors at the crossroads of crucial issues: food security, environmental sustainability, demographic growth, economic competitiveness, and resilience to climate hazards. These sectors are inherently exposed to uncertainty: variability of yields, dependence on weather conditions, market fluctuations, instability of supply chains.
In this context, artificial intelligence provides a structuring technological response by allowing for the modeling of complex biological systems, processing real-time data from multiple sources (satellites, sensors, drones, climate data), and making optimized decisions at every step of the agricultural or agri-food cycle, from the field to the plate.
It acts as a vector for anticipation, optimization, and security: forecasting yields, early detection of diseases, adjustment of inputs, harvest automation, quality control, product traceability, reduction of waste. In this regard, it constitutes a fundamental lever for the transition to precision agriculture and an intelligent, resilient, and sustainable agri-food chain.
What NeuriaLabs Brings to the Agricultural and Agri-food Sector
NeuriaLabs provides stakeholders in the agricultural world (farmers, cooperatives, agricultural chambers), the agri-food industry (processors, logistics providers, distributors), as well as public institutions responsible for food and environmental policies, advanced artificial intelligence solutions aimed at transforming data into operational, ecological, and economic advantages.
We support stakeholders in the sector by assisting with the collection, modeling, interpretation, and action on data from the natural environment, agricultural machines, processing facilities, and logistics chains.
Our approach aims to enhance productivity while reducing environmental impact, ensuring end-to-end traceability, optimizing the use of inputs (water, fertilizers, pesticides), and increasing the capacity to respond to extreme events (drought, diseases, shortages, price fluctuations).
Use Cases in Agriculture and Agri-food
The use cases of artificial intelligence in this sector are varied and cover agricultural, industrial, environmental, and logistical dimensions:
• Agricultural yield forecasting: modeling production potential by plot, incorporating agronomic, climatic, satellite, and historical data.
• Automated detection of diseases and plant stress: analysis of images from drones or satellites to early identify signs of infection, deficiency, or water stress.
• Dynamic adjustment of inputs: intelligent management of volumes of water, fertilizers, or pesticides based on actual needs, reducing costs and environmental impact.
• Automation of agricultural tasks: autonomous operation of machines (tractors, harvest robots) via embedded AI, crop recognition, localization of areas to treat.
• Quality control in agri-food processing: automated detection of defects, contamination, or non-conformities on production lines using industrial vision.
• Optimization of stock and logistics flows: demand forecasting, management of perishable stocks, adjustment of distribution circuits based on weather, maturity of products, or local demand.
• Intelligent traceability of the value chain: automated tracking of batches from production to distribution, integrating AI, blockchain, and processing of regulatory data.
Solutions Developed by NeuriaLabs for the Agricultural and Agri-food Sector
NeuriaLabs designs vertical artificial intelligence solutions for agricultural and agri-food sectors, interoperable with embedded agricultural systems (machinery, IoT sensors), production management software, agri-food ERPs, and logistics systems.
Our solutions include:
• Platforms for agricultural yield forecasting using geospatial AI: integration of satellite, weather, soil, and historical data, coupled with supervised learning models, to generate production estimates at the plot or regional scale.
• Systems for automated detection of plant diseases: vision modules applied to multispectral imaging capable of identifying at-risk areas, classifying symptoms, and proposing targeted intervention strategies.
• Engines for optimizing agricultural inputs: intelligent control algorithms that regulate in real-time irrigation, fertilization, or pesticide treatments based on data from the field and yield or sustainability objectives.
• Modules for assistance in agricultural robotics: AI embedded in equipment (autonomous tractors, harvesting robots, smart sprayers), enabling recognition of agricultural objects, assisted navigation, and autonomous management of interventions.
• Quality control systems in processing plants: deployment of industrial cameras combined with convolutional neural networks for defect detection, product classification, and automated rejection of non-conformities.
• Logistics management tools for perishable products: predictive solutions for adjusting stocks, planning deliveries, and optimizing cold chain management, taking into account conservation constraints, delivery times, and maturity cycles.
• Solutions for enhanced traceability through AI: platforms incorporating automatic batch recognition, document interpretation, detection of supply chain interruptions, and generation of automated certificates of compliance, serving food and regulatory transparency.
Our solutions are designed in compliance with current agricultural, health, and environmental regulations (HACCP, ISO 22000, GlobalG.A.P., European regulatory traceability), and can be deployed at the scale of a farm, a cooperative group, or an entire sector.
