Innovation of Smart Farm with CLICK AI

Variety, Site Conditions, Crop and Livestock Management or Analysis

CLICK AI analyzes vast amounts of varieties, weather, and sensor data from cultivation to processing, manufacturing, distribution, and sales, making decisions on how to streamline agriculture clear and optimized so that agriculture can be transformed into knowledge-based agriculture.

It is important to make the right decisions quickly. In a typically one-way manufacturer's process, planning and data can be organically linked to each stage via CLICK AI, enabling rapid and agile development.

  • Do you want to be ahead of change?
  • Do you want the best thought and knowledge?
  • Do you want to move fast and agile to keep up with the changes?
  • Do you want to make decisions based on high-quality information at high speed?

Use Cases

Selection of Variety

The first thing to consider when starting farming is what kind of varieties you choose to cultivate. Species selection is a complex process that requires finding specific genes that determine the effects of water and nutrient use, adaptation to climate change, disease resistance, nutrient content or taste. The deep learning of CLICK AI model uses decades of field data to analyze crop performance in various climates or soils and new characteristics developed in processes. Based on this, you can predict which genes can give plants a favorable trait.

Soil Management

For agricultural experts, soil is a heterogeneous natural resource with complex processes and ambiguous mechanisms. Weather data alone can provide insight into the impact of climate change on local production. The artificial intelligence model using CLICK AI can make soil management more efficient by analyzing the evaporation process, soil moisture and temperature to understand the dynamics of the ecosystem and the impact of agriculture.

Yield Forecasting 

Yield forecasting is one of the most important topics in precision farming because it defines yield mapping and estimation, matching crop supply and demand, and crop management. The deep learning of CLICK AI not only predicts future yields based on accumulated past data, but also enables comprehensive multidimensional analysis of crops, weather and economic conditions through visualization to make the most of the yields.

Disease Detection

The most widely practiced practice for pest and disease control in outdoor and greenhouse conditions is the application of pesticides to crops. To be effective, spraying of pesticides requires a significant amount of pesticide, which incurs high financial and environmental costs. The artificial intelligence of CLICK AI can reduce financial and environmental costs by efficiently and evenly spraying pesticide using data on crops affected by the time, location or pesticide injection of pesticides.

Livestock Production Optimization

Like crop management, deep learning can provide accurate predictions and estimates of agricultural parameters to optimize the economic efficiency of livestock production systems such as livestock and egg production. When using an artificial intelligence model using CLICK AI, you can optimize livestock production by predicting the condition or weight of livestock in the future using accumulated livestock data and modifying the condition individually.

Weed Detection

Weeds are one of the greatest risks to crop production. The biggest problem in the fight against weeds is the difficulty in detecting and distinguishing between crops and weeds. A deep learning algorithm using CLICK AI can detect and identify weeds at low cost without environmental problems and side effects. This technology eliminates weeds and minimizes the need for herbicides through the composition linkage with the robot.

Variety Recognition

The traditional human approach to crop classification is to compare the color and shape of the leaves. Image classification or object detection using the deep learning of CLICK AI not only makes the traditional method faster and more efficient, but also provides more accurate and faster results by analyzing the leaf vein shape that conveys more information about leaf characteristics.

Crop Quality Classification

Accurate detection and classification of crop quality characteristics can increase product prices and reduce waste. The deep learning model of CLICK AI can efficiently classify and detect the quality of all crops by utilizing the quality characteristic data of past crops.

Supply Chain Optimization

Research shows that 79% of companies with good performing and optimized supply chains have achieved greater sales growth. Deep learning helps you optimize your supply chain by identifying problems across individual and collective supply networks, and streamlining operations to address supply network variability. Artificial intelligence models trained through CLICK AI continuously analyze supply chain data to find new patterns. This pattern provides an opportunity for companies to optimize their supply chain processes by identifying with predictive accuracy a set of factors affecting the supply chain.

Forecasting Maintenance

CLICK AI can accurately predict asset failure by aggregating data from sensors, device enterprise systems, and operating systems. CLICK AI provides planners and operators with a comprehensive insight into asset risk to maintain higher asset availability, service-based differentiation, and lower maintenance costs.

Safety Monitoring

Deep learning can be used to analyze and predict video and image-based data to detect and warn safety risks in real-time. Customers can remotely verify remote operational security and detect asset integrity through upstream, midstream and downstream operations. You can avoid dangers through fast processing and feedback in the field by monitoring risks such as smoking behavior with image classification or object recognition AI using CLICK AI.

Production Optimization

When the factory is measured with IOT, the available data will surge. CLICK AI enables operators in digital factories to use data insights to identify potential production refinement and predict quality, cost and throughput. As a result, waste and disruption can be reduced and yields can be improved to develop operational efficiency."

Consult with an expert now.

Consult with an expert now.