Hyderabad: Agriculture, the oldest vocation known to mankind, has survived the onslaught of time quite gracefully. Surviving multitude of challenges, agriculture today has transformed into a more refined and elaborate version of its old self. From climate changes to an ever-increasing human demand for quality and quantity, the sector seems to recalibrate and moved forward, robust and steady, with some support from the marvellous technological innovations. India, for instance, was a country which depended on food imports for the day to day survival. But today we lead in food production meeting the nation’s food security, but also that of the rest of the world. Thanks to the green revolution and the technological advances that followed, our food grain production stands at a record 300 million tonnes, from the paltry 50 million tonnes of the pre-green revolution period.
Technology has remained an integral part of Indian agriculture since then, either as improved inputs, cropping systems or crop care. Penetration of Information and communication technology has further fine-tuned the concept of agriculture, as they have become more precise, productive and sometimes virtual. So the advent of Artificial Intelligence (AI) into the agricultural domain does not come as a surprise.
AI is a cognitive process one can associate with human thinking but which is more precise and accurate. It enables automation of decision making, thus reducing human intervention. Thus AI becomes technically very relevant in agriculture, which itself involves decision making at multiple levels. The human involvement in decision making makes farming vulnerable to errors and oversights. Farming of the future cannot afford the lapses as we tread the delicate path of ensuring food security to the consumer and income security to the farmers. The world has started to embrace AI-powered support in agriculture wholeheartedly.
A market study report suggests that the market size of global Artificial Intelligence (AI) in Agriculture is expected to reach 1550 million US$ by the end of 2025. AI finds use in agriculture at almost all stages, but most importantly in crop selection, crop monitoring and prediction. Climate is an inextricably linked component of the agriculture system in India. With rainfed agriculture contributing to 60 per cent of the value of agriculture GDP of India, it becomes imperative to have backup plans to navigate a potential aberrant weather phenomenon. Overly moist soil or persistent dry weather may no sometimes permit the successful farming of the conventional crop of a particular area. AI becomes useful in suggesting an alternative crop for that particular region taking into consideration a variety of factors like prevailing climatic conditions, input availability, local preferences, market prices and market demand. Microsoft in collaboration with the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) has deployed a Sowing Advisory Service in the Kharif season on a limited pilot, under the 'Bhoochetana' project. The app sends sowing advisories to participating farmers on the optimal date to sow.
A step further, complete crop monitoring and soil monitoring unveils another interesting facet of AI in agriculture. Image-based solutions provide insights on the crops’ health during the growing season and furthermore provide the farmers with options to manage a potential risk. Using deep learning and image processing models, crop diseases or pest infestation can be identified in the crops. Along with the parameters, AI-based models can give recommendations on how that disease can be cured and prevented from increasing
further. Image recognition and deep learning models have enabled distributed soil health monitoring without the need of laboratory testing infrastructure. AI solutions integrated with data signals from remote satellites, as well as local image capture in the farm, have made it possible for farmers to take immediate actions to restore soil health.
Agri Export Policy 2018 was an important intervention by the government that had broadened the market prospects of Indian farmers. However, this also meant bringing a standard into the production system in India in terms of grading. AI-enabled systems can help in creating an international agri-commodity standard for reliable trading across country boundaries. Automated quality analysis of images of food products is an accurate and reliable method for grading fresh products (fruits, grains, vegetables, cotton etc.) characterized by color, size and shape. AI-enabled technology reads the image that a farmer has taken on his phone and determines the product quality in real-time, without any manual intervention.
Predictive analysis is another area where AI can serve agriculture. The government of Karnataka had signed an MOU with Microsoft to empower smallholder farmers with AI-based solutions through price forecasting. Microsoft with guidance from Karnataka Agricultural Price Commission (KAPC) would be using digital tools to develop a multivariate agricultural commodity price forecasting model considering parameters such as sowing area, production time, yielding time, weather datasets, and other relevant datasets.
Microsoft has also partnered with United Phosphorous (UPL), India’s largest producer of agrochemicals, to create the Pest Risk Prediction App that again leverages AI and machine learning to indicate in advance the risk of pest attack.
In its June 2018 discussion paper, titled ‘National Strategy for Artificial Intelligence’, NITI Aayog itself has acknowledged the significance of AI in India, a country with the fastest- growing economy and hosting the world’s second-largest population. Five sectors have been pointed out by NITI Ayog which can place India among the leaders on the global AI map, out of which incidentally, agriculture emerges as an important front.
Agriculture practised in India is a sum total of traditional wisdom, conventional farming practices, policy interventions and a certain amount of unpredictability. Climate, politics, international state of affairs, natural disasters add to the fickleness of the sector.
Technologies like AI have the capability to arrive at solutions considering a variety of diverse reasons influencing farming thereby reducing the errors in judgment and in some cases giving information beforehand which are useful in avoiding farm losses. AI will mark a new phase in agriculture, the exactness of the technology may be a little overwhelming in the beginning, but in the end, we may finally be able to meet the demand-supply equation with certitude.
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