From automated pest and plant disease detection to intelligent spraying and produce sorting — Artificial Intelligence is transforming the food and agriculture sector.
SECOMIND Crop Manager enables profitable and sustainable agricultural production.
One Agricultural Revolution happened about 12000 years ago, and other is happening right now driven by AI SECOMIND is PROUD to be participating in it.
Remote Manageability & Telemetry; Farmers/Businesses, Finances, Inventory Dashboards, Reports, & Alerts
Sprayer Analytics, Sprayer Automation, Crop Analysis, Weed Classification, Pesticide Recommendation & more
Add on or integrated equipments that enabled remote management and intelligence
Customize all mobile apps, web interfaces, DNS/domain name for your brand and customize theme that fits your brand.
Our software is designed with API first approach. You can integrate our system with other systems easily!
Grow your business with state of the art security and 24x7 support.
Real time weed detection and spray control optimization to reduce or eliminate herbicide use, mitigating agricultural environmental and health impact, and improving sustainability.
Prediction of weed emergence periodicity can help farmers implement more informed weed control strategies. Timing of weed control measures is important for optimum weed control - preemergence (PRE) herbicides as well as postemergence (POST) herbicides.
Monitor the health, well-being and nutritional status of a cow or group of cows for early disease warning, identifying health problems with precision. Solution provides better individual cow treatment saving time and costs.
Classifying the types of pest to recommend the most suitable pesticide according to the type of pest. Available as a mobile app to assist the farmer.
The identification of plant disease is an imperative part of crop monitoring systems. An accurate and an early detection of diseases and pests in plants to help to develop an early treatment technique while substantially reducing economic losses.
Crop production assumptions made far in advance can help farmers make the necessary planning. In this application, we do crop yield forecasting at the field level using current crop health, climate variables, and other mathematical models.
And, many more applications!
