It’s 2021 and Bhuvan and his ensemble (from the film Lagaan, 2001) are free of debt, no longer worry about paying taxes to the British, (and have learnt the game of cricket). However, weather predictions and agriculture monitoring including crop identification and yield estimation continue to grapple the team.

Indian agriculture faces multiple challenges like dependence on monsoon, heavy use of resources (like water, inorganic fertilizers and pesticides), loss of soil fertility, land degradation etc. Given that the country continues to heavily depend on agriculture contributing to the overall economic growth and employing almost 50% of the entire workforce, it is essential to improve and contribute to the sector, especially through technology. 

Forward-thinking startups like SatSure, a technology-agnostic platform, come as a breath of fresh air for the sector by using satellite image analytics, cloud computing, big data and AI. In addition to mechanizing agriculture, they also serve the banking and insurance sector, aviation and defence domains and add predictive analysis to climate change.

The plight of the modern farmer

The population is soaring and with a high dependency on the agricultural sector, we are and might continue facing a crisis. Some of the major challenges faced by the sector are as follows:

• Despite having 300+ active Earth-imaging satellites, the optical satellite imagery lacks affordability and visibility. The revisit time is high and cloud covers reduce the area available for analysis and monitoring
• SAR(Specific Absorption Rate) data which serves as an alternative to satellite imagery is complicated 
• Gaining insights into weather conditions, soil monitoring, crop behaviour prediction, environmental conditions like insects or birds, optimum use of pesticides and fertilizers etc. or even maintaining and referring to records is almost impossible manually
• Aspects like the financial success of the crop, crop modelling, yield estimation and crop identification are difficult to predict
• Crop monitoring and agricultural management at scale is impossible without technological interventions and advancements that might aid the process

A data-driven approach and eventually automation of certain processes are therefore essential to advance agricultural development.

Data-backed predictive analysis for the agricultural sector

Machine Learning, AI, and cloud-based technology can play an essential role in helping scale up the process both with increased efficiency and pace and reduce the burden on the supply chain.

With deep learning satellites like those promised by SatSure that are tuned to provide accurate data thrice a week information is collected continuously and predictions become clearer. This helps in understanding crop behaviour and gauging weather thereby increasing farmer efficiency and maximizing the ROI. Furthermore, being able to store this data on the cloud helps understand the behaviour from a long-term perspective and therefore make more calculated predictions for the farmers.

Having AI-based technological solutions like this enable the farmers and help produce more with less input while also improving the quality of output. The highlight, however, is accurate image reconstruction through satellite imagery, at scale. It opens up a whole new world of data points that can be captured in real-time. This helps in predictive agricultural analytics wherein machine learning tools can predict the right time for yield, to produce at the right time, and to improve the quality of their crops by understanding and optimizing external conditions.

Panpatte(2018) said that artificial intelligence makes it possible for farmers to assemble large amounts of data from various sources and provides farmers with data-based solutions, smart methods of farming and irrigation thus resulting in higher yields. In other words, it enables the farmers with what they do not know and by combining this with what they do know, i.e. combining technological and biological skills they will be equipped to transform the sector substantially. Technology has already penetrated all sections of the society including farmers who have embraced the smartphone era already. Adding the technical expertise to predict crop behaviour using satellites or data-based analytics will be a seamless and logical next step.

Data-Farming

It is estimated that by 2050, average farms will generate 4.1 million data points every day. With automation, AI and predictive analysis becoming a part of their lives, farmers are all set to become more productive and less ambiguous and dependent.

As for Bhuvan and the team (and their likes across the country), feeding over a billion stomachs will require the classic blend of technological and experiential skills to make that leap in agricultural productivity. And with AI-based solutions, and data-backed farming methodologies they will hopefully know exactly when to sing ‘ghanan ghanan’ next!

• Forbes. https://www.forbes.com/sites/louiscolumbus/2021/02/17/10-ways-ai-has-the-potential-to-improve-agriculture-in-2021/?sh=184b56ef7f3b
• Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. (n.d.). ScienceDirect.com | Science, health and medical journals, full text articles and books. https://www.sciencedirect.com/science/article/pii/S258972172030012X
• Singh, A. (2020, November 26). AI for the farmer. The Indian Express. https://indianexpress.com/article/opinion/columns/artificial-intelligence-farmer-agriculture-7069520/\
• Image from International Maize and Wheat Improvement Center via Flickr