From "Relying on the Weather" to "Farming with Foresight" | JHCTECH Agricultural Monitoring Solution Based on BRAV-7120
In traditional agricultural production, assessments of crop growth have long relied on manual observation and individual experience. However, this approach faces two major challenges. On one hand, human observation is limited in its ability to detect subtle changes in crop conditions, making it difficult to accurately assess soil moisture levels or identify early signs of pests and diseases. On the other hand, while smart agriculture technologies continue to advance, large-scale intelligent systems often come with price tags reaching hundreds of thousands, placing them beyond the reach of most small and medium-sized farms.
How can farmers overcome the challenges of inaccurate experience-based judgments and prohibitively expensive smart equipment within a limited budget? JHCTECH’s AI edge computing system, BRAV-7120, is emerging as the “intelligent brain” of next-generation farmland monitoring stations. With its strong computing performance and efficient power consumption, BRAV-7120 brings professional-grade agricultural monitoring capabilities directly to the field.
Solution Core: Why BRAV-7120?
Hardware Foundation: Built for Harsh Farmland Environments
✔ High Computing Power with Low Power Consumption
With AI computing performance ranging from 20 to 67 TOPS, BRAV-7120 enables offline edge deployment of various AI models, including crop disease recognition, pest monitoring, and weed classification. Its 7–15 W low power consumption, combined with a 9–36 V wide-voltage input, makes it ideal for solar-powered deployments, solving power access challenges in open farmland environments.
✔ Vision Acquisition System
Up to 4 network ports support connections to wide-angle, multispectral, and infrared cameras, providing multi-angle coverage of farmland for early disease detection and warning. The system also supports GMSL cameras, enabling long-distance, low-latency visual acquisition and monitoring.
✔ Multi-Sensor Integration
Equipped with multiple serial ports and CAN, it can integrate a series of soil sensors, including multi-layer soil moisture, temperature, and conductivity sensors, to monitor key parameters such as soil moisture content, salinity, and soil temperature in real time. These sensors act like "electronic tentacles" inserted into the soil, continuously providing farmers with soil "health reports."
✔ Software-Based Synchronization Mechanism
The 8-bit DIO supports 1–30 Hz pulse output to trigger external sensors for synchronized sampling, ensuring time-aligned data acquisition. This provides a reliable foundation for multi-sensor data fusion and analysis.
✔ Rich Network Expansion
It supports expansion with 4G/5G and Wi-Fi modules for wide-area communication and real-time integration with cyber-physical systems. In regions with limited network coverage, LoRa communication can be deployed for data transmission, offering high sensitivity and excellent power efficiency, making it particularly well suited for rural agricultural environments. Collected data can be transmitted to the cloud or directly to farmers’ mobile applications, enabling remote monitoring without on-site presence.
✔ Compact & Rugged Design
It operates in temperatures ranging from -25°C to 60°C, adapting to harsh farmland environments. Its compact design and optional fanless configuration further reduce maintenance requirements.
Software Layer: Simplifying AI Development and Deployment
💡 Optimized AI Models for Enhanced Efficiency
By optimizing object detection models such as YOLOv5 and removing unnecessary detection scales, computational requirements can be significantly reduced without compromising accuracy. Internal optimization results indicate up to a 30% reduction in model parameters and a 35% improvement in inference speed.
💡 Multimodal Data Fusion and Analysis
The monitoring station fuses and analyzes visual data and sensor data to generate comprehensive farmland health reports. For example, by combining drone-based multispectral imagery with ground-level soil nitrogen data, the system enables more accurate prediction of crop yield and overall crop health.
💡 Pre-Built Software Stack
The BRAV-7120 supports the NVIDIA AI software stack and application frameworks for specific use cases, such as CUDA, Isaac for robotics, and DeepStream for computer vision. Developers can also use the TAO-KIT toolkit to fine-tune pre-trained models, significantly improving the efficiency of AI application development.
BRAV-7120 Transforms Farmland Management From "Relying on the Weather" to "Farming with Foresight"
Tangible Insights: Visible Efficiency Gains
⭐Precision Irrigation Guidance: BRAV-7120 delivers precise recommendations on irrigation timing and water volume based on soil moisture data. In a case study from Northeast China, the monitoring station alerted farmers with guidance such as: “Soil moisture at the 10–20 cm layer is below the standard for the corn seedling stage. Drip irrigation is recommended early tomorrow morning.” This provides clear, data-driven operational guidance.
⭐Early Pest & Disease Detection: Through visual analysis, the system detects early signs of pests and diseases and issues timely alerts. In practical deployment, the system identified a corn borer risk in a monitored field. Agronomists then guided the application of biological pesticides, reducing pesticide usage by 30% compared with traditional control methods—delivering both environmental benefits and cost savings.
⭐ Growth Monitoring & Yield Forecasting: Integrating multispectral imagery analysis and plant height tracking, the system precisely monitors crop development, providing actionable insights for fertilization and management decisions.
⭐ Cost Reduction & Profit Enhancement: Based on customer-provided data, fertilization strategies were optimized across a 60-mu pilot field, resulting in an estimated increase of RMB 150 per mu in yield value. In addition, the solution supported a 150,000-mu agricultural demonstration zone, helping reduce overall annual production costs by more than 20%.
Deployment and Promotion: Simple and User-Friendly, Easy to Get Started
Quick Installation: The monitoring station features a compact design, requiring only simple mounting and sensor placement to become operational without disrupting normal farm operations.
User-Friendly: It offers an intuitive graphical interface, allowing farmers to operate it conveniently via tablet. An LED display can also be installed in the field to show key data in real-time, benefiting all farmers.
Low-Cost Operation: Powered by solar panels and efficient energy management, the monitoring station achieves long-term autonomous operation with minimal maintenance.
Scalable Expansion: Farms can start with basic monitoring functions and gradually add more intelligent analysis modules based on actual needs and budget.
Looking ahead, JHCTECH will continue to deepen its expertise in edge computing, creating more standardized solutions for vertical industries. We will work closely with our ecosystem partners to promote the large-scale implementation of intelligent technologies across various sectors, providing a solid foundation for industrial upgrading.