Last updated on February 18th, 2021 at 10:43 am
Agriculture drives the Indian economy with a whopping population of nearly 70% in rural areas and 40% being part of the agricultural workforce. However, it has many issues and hurdles in realizing its full potential and leveraging analytics and technology for it. The sector lacks banking, financial, disaster management, and water inadequacy facilities and infrastructure. Also due to lack of education migration to cities is a major issue. Though in the early stages the policymakers were quick to realize the potential of analytics and technology in mitigating the hardships of farmers and slowly but steadily the combination is appearing to slow down and address the agriculture segment pressing issues.
Use of Big Data Analytics:
Data is the life breath of all activities in modern times and in agriculture too. Leveraging the potential of analytics and Big Data can bring about immense changes in agriculture and its productivity. The frequent news-releases on droughts, crop failures, farmer suicides and such acute results of backward farming and agriculture stresses the need for the involvement of technology and big data in improving the lot of the farmers and agriculture segment. Be it crop patterns, wind directions, crop loss mitigation, soil adequacy, and fertility, it is Big Data analytics that has offered solutions using technologies like
Cloud and Nanocomputing
Big data, digitalization and visualization use.
AI, IoT and ML use.
Saas Platforms, cloud services, and web-based apps.
Role of data and the data analyst:
Agriculture is interdisciplinary and combines concepts of business management, chemistry, mathematics, statistics, physics, economics, and biology. Like all interdisciplinary sectors, the need for data and its use is crucial for growth, change, and development. This means that like in other segments the data analyst role is both well-paying, has an unending scope and relies on a variety of latest futuristic technologies and smart apps.
Knowledge of sciences, agriculture methods, biotechnology, animal and soil sciences, etc will definitely aid the analyst. The analyst will also need proficiency in analysis techniques, data prepping and predictive analysis.
Analytical technologies in the agriculture sector can be used effectively in
Capturing data: using the IoT, biometrics, sensors, genotyping, open and other kinds of data, etc.
Storage of Data: using data lakes, Hadoop systems, Clouds, Hybrid files and storage, etc.
Transfer of Data: via wireless and wifi, linked free and open source data, cloud-based solutions, etc.
Analytics and Transformation of data: through ML algorithms, normalization, computing cognitively, yield models, planting solutions, benchmarks, etc.
Marketing of data and its visualization.
What is Smart Farming?
Smart Farming uses analytics, IoT, Big Data and ML to combine technology and agriculture applications. Farming solutions also offer
ML and data visualization techniques.
App-based integration for data extraction and education.
Monitoring through drones and satellites.
Cloud storage for securing large volumes of data.
Smart Farming technologies and analytics can thus be efficiently used for forecasts, predictions for better crop harvests, risk mitigation, and management, harvest predictions, maximizing crop quality, liaising and interconnectivity with seed manufacturers, banks, insurers, and government bodies.
What is Precision Agriculture?
This methodology is about Crop Management which is site-specific and also called ‘Farming using Satellites’. The information from satellites helps distill data regarding topography, resources, water availability, the fertility of the soil, nitrogen, moisture and organic matter levels, etc which are accurately measured and observed for a specific location or field. Thus an increase in ROI and optimization of resources is possible through satellite aided analytics. Other devices like drones, image files from satellites, sensors, GPS devices, and many more can prove to be helpful aids and are fast becoming popular.
Concluding with the challenges:
Though the technologies are the best the implementation and applications to the agriculture sector are lacking. Education and training of the farmers is the best solution but involves a lot of man-hours, uninterrupted power, use of data efficiently, internet connectivity, and finance to help these measures succeed and develop to their full potential. Right now it is in the nascent stage and the need for data analysts is very high. To get the best skill development training courses in data analytics do try Imarticus Learning which is a highly recommended player with efficient, practical skill-oriented training and assured placements as a bonus. Where there is a will the way will show up on its own. Hurry and enroll.
