There is a wide potential and scope for AI-assisted agriculture as their application is still very limited and in the nascent stage. A lot needs to be explored in terms of the impact areas of AI in precision farming and agriculture as a whole.

With the rising global population, the focus of the world has come to issues of food security and climate change. We all know that sustainability was much-ignored term till a few years ago but is now exigent. Governments and agricultural organisations around the world are deploying AI in agriculture intending to tackle issues such as increasing productivity, improving efficiency, addressing labour shortages, and most importantly addressing sustainability issues.

In the last 20 years, there have been remarkable innovative developments in amalgamating IT in agriculture practices. Agricultural Information Technology (AIT) is an effort towards achieving the United Nations’ Sustainable Development Goals of zero hunger by 2030. The agriculture sector had been in drastic need of improvements owing to the disruptions caused by climate unpredictability, greenhouse gas emissions, water crisis, and natural calamities. Artificial intelligence is bridging the gap between conventional agricultural practices and efficient, productive, and sustainable farming methodology while reducing the negative economic, socio-economic, and environmental impacts. 

We can broadly divide the AI intervention areas in agriculture into the categories such as Data Collection and Analysis, Surveillance and Image recognition, chatbot assistance to farmers, crop and seed quality selection based on farmers’ needs, and workload sharing via automation.

Data Collection

AI has paved the way for precision farming which is expected to change the game by assisting farmers in making informed decisions using data-intensive tools. Precision farming is based on the concept of AI wherein huge amounts of data are collected which later is churned into valuable information related to agricultural practices. Indian AgriTech startup Aibono, is providing next dimension food and farming solutions such as real-time precision agriculture and real-time synchronisation of supply and demand using AI.

Surveillance and Image Recognition

Machine learning (ML) and AI together provide insightful information on soil, weather, water which are critical factors for irrigation, plantation, and harvesting. These aspects actually determine the crop yield and quality while taking care of sustainability. Intello Labs, an AgriTech startup is using AI-enabled image processing to access food quality at the source. Such startups are working closely with food growers, processors, retailers, and foodservice companies along with other stakeholders in the food supply chain.

Workload Sharing

Agricultural robots are programmed to deliver in areas such as irrigation, soil content sensing, crop monitoring, weeding, and many more. This enables farmers to produce faster-to-market crops with reduced effort. With AI-assisted drones crop monitoring, surveillance, forest fire detection becomes much easier providing a breather to farmers. As stated by the UN, by 2050, 2/3rd of the world's population will be living in urban areas owing to which there is a need to relieve farmers of the burden and hence the need for AI-assisted automation to reduce risks and increase productivity. 

Data Analysis and Forecasting

Various software and ML algorithms provide a correlation and analysis on soil defects, foliage patterns, weeds, and plant pests. Fasal, an Indian AgriTech uses an AI-based microclimate forecasting algorithm that incorporates real in-field information. This information is then correlated to publicly available weather forecasts. This helps farmers benefit from real-time, actionable information making their daily farm operations much resolved and precise. The AI-assisted weather forecasts, disease detection, custom irrigation schemes along with farmer’s expertise and intuition can alter the scenario for better.

Chatbots

Globally chatbots are providing farmers interactive sessions to impart knowledge about best crop selection, hybrid seed choices, seed choices as per weather conditions, soil types, plant diseases, and increase the return-on-crops using deep learning. DeHaat, another player in the AgriTech sector is offering 24/7 support to small farmers on how to grow and where to sell affordably. The company is operating majorly with farmers in states of UP, Odisha, Bihar, and Jharkhand.

Moreover, we can also say that AI-based strategies can sense market price fluctuations and provide signals towards better planting and harvesting. Many AgriTech startups are helping farmers in these tough COVID-19 phase where logistics and supply chain were disrupted beyond control. As per Confederation of Indian Industry (CII), only 6% of farmers in India get benefits of Minimum Selling Prices (MSP).

It can be deduced that the deployment of AI can provide agricultural firms with a competitive edge while ensuring sustainability by reducing wastage and protecting and realigning resources. Governments of developing nations in Asia are keen on investing in AI to increase sustainability and profitability both environmental and operational. There are more than 500 AgriTech startups in India which are leveraging technologies like AI and ML for improving India’s agricultural growth. Predictive modelling using AI can be instrumental in presenting more accurate demand-supply information and a better price realisation is possible for farmers through the price discovery model.

Inhibitions surround the AI intervention in agriculture and the fact that we need specialised skills to leverage AI along with the nascent stage that this duo is in. However, the scope is boundless considering the scenario where agricultural organisations align their long term objectives and accordingly acquire or rent relevant AI services. Agriculture in our country in the past has been lacking the attention it deserved attention which seems to be changing with the focus of young bright minds (Innovative ideas that startups are coming up with) and governments. This looks promising in addressing both the productivity issues along with the environmental concerns.

Image by George Hodan from Public Domain Pictures