Integrated Predictive Analytics (IPA)

Integrated Predictive Analytics (IPA) is a modern technology platform involving the application of advanced analytical methods and techniques to identify the composition of feed traits and prediction of agribusiness performance and operation.

Basically, to measure and evaluate the entire process from RGB i.e. multispectral imaging to the map by utilizing ground-breaking approach.

IPA is seamlessly integrated into several crop phases, integrated with wireless sensors, high-resolution satellite and aerial imagery, field, and ancillary data.

One of the important usages of IPA is to estimate crop production, assessment of plants health and identification of methods to maximize crop yield.

IPA also advances as a predictive analytics service to preserve the ecosystem while enhancing the crop yield. It also efficiently combines all the analysis and results in the form of report which can be stored and shared with clients in no time


Multitude of sensors gather huge data and transfer from the site to the Cloud based Centralized AI Engine for Cross-Validation


Trigger deep-learning algorithms to analyse and process data (through Advanced Multi-Parameter Crop Model)


Developed to track and predict:
◉ Crop Yield Prediction
◉ Crop Damage Assessment
◉ Field Soil Analysis and Irrigation Planning


Program and plan approach for mitigating the lossage and boost productivity by providing farmers the right intel

IPA achieves crop health monitoring using deep learning algorithm based on real-time data from sensors, satellite/drone images and on ground farming practices. Identification of potential defects in soil-plants and farming activity recommendations that includes crop/ variety selection, crop protection and timings (sowing, irrigation, harvest). AI algorithms are used to predict yield, crop quality, input demand and output aggregation.

Actionable insights to make data driven decisions for meeting the relevant food demand. It revolves around analyzing humongous data from multiple parameters which can be integrated with agricultural knowledge from different resources. This has led to the creation of integrated models, crop growth models, water balance models, soil nutrition models, farm optimization models and risk assessment models.

A system for monitoring the crop field with the help of sensors (light, temperature, humidity, soil moisture etc.) while automating the irrigation system. The field condition can also be monitored by farmers from anywhere which highly supports efficient Smart Farming. The benefits include efficient water usage and optimization of input/treatments.

Application of drones, Unmanned Aerial Vehicles (UAV), Unmanned Ground Vehicles (UGV) to create a smart integrated system through which real time crop health can be assessed. Drones can be used for automatic spray of nutrients/inputs. The benefits help to increase crop production and farm efficiency.

Promotion of sustainable farming by using technologies which can be helping to identify the crop patterns for recommendation. The crop dependent Farm Calendar is provided where we ensure the date wise activities to be monitored to achieve better production with less cost of cultivation.

Application of Machine Vision for automation/ guidance of smart farming devices integrated with tractors or separate robots in some cases for harvest, plantation, weeding etc. The advantages are its usefulness in identifying/ sorting/ grading the best crops from bad crops and determination of stable for longer logistics and lower level quality should be passed onto local market.

Data Sets and
AI Generated Maps and Assesments