Tech edge essential for boosting farmers’ income
VP Sethi
DIGITAL technologies such as machine learning (ML), the Internet of Things (IoT), artificial intelligence (AI), robotics, drones and machine vision are playing a significant role in modernising Indian agricultural processes. These technologies have the potential to revolutionise the agricultural sector by optimising resource utilisation, enabling precision agriculture, and improving productivity, efficiency and sustainability.
By analysing data from various sources, AI can help farmers make data-driven decisions. The World Economic Forum has reported that AI integration in agriculture can bring about 60 per cent decrease in pesticide usage and 50 per cent reduction in water consumption. In India, enhancing 15 agriculture datasets, such as soil health records, crop yields, weather, remote sensing, warehousing, land records, agriculture markets and pest images, can lead to a $65-billion opportunity, according to research conducted by Nasscom and McKinsey.
Making INdian agriculture modern & smart
- The use of modern machines, including kisan drones, being promoted under Sub-Mission on Agricultural Mechanisation
- Start-ups being encouraged to use innovative technologies to deal with challenges in agriculture and allied sectors
- Under NeGPA programme, funds given to state governments for digital agriculture projects using emerging technologies like artificial intelligence, machine learning, the Internet of Things, etc
- Innovation & agri-entrepreneurship being promoted under Rashtriya Krishi Vikas Yojana by providing financial support and nurturing the incubation ecosystem
AI can be used to analyse large amounts of data to provide valuable insights to farmers. For example, AI-powered systems can guide farmers by analysing weather patterns, soil conditions and crop health, identify disease outbreaks and optimise irrigation scheduling. This can lead to improved crop yields and resource allocation. AI can also be used for predictive analytics to forecast market demands and maximise profitability. Additionally, AI-powered drones can be used for crop monitoring, disease detection and precision agriculture, allowing farmers to take timely and accurate actions.
The ML process is a data-driven approach to finding patterns in data that can be used to make predictions. The process starts with collecting data, cleaning it and preparing it for analysis. Next, various ML algorithms are applied to the data in order to find patterns. Finally, the results of the analysis are used to develop predictive models for agricultural factors such as crop yield, pest and disease outbreaks and optimal planting times. ML can also be used to develop personalised recommendations for farmers based on their specific land conditions, crop choices and market trends. For example, a start-up in India has developed a machine learning-powered platform that processes data from IoT sensors, weather forecasts and soil conditions to provide recommendations to farmers regarding crop selection, planting schedules and resource management. This technology empowers farmers to take informed decisions and optimise their farming practices.
The IoT allows stakeholders to measure important parameters, including soil moisture, air temperature and crop health. This data can help farmers identify patterns and make predictions. This data can be used to optimise irrigation schedules, monitor crop growth and detect potential issues early on. IoT-enabled smart sensors and actuators can be integrated into farming equipment and infrastructure to enable remote monitoring and control, leading to improved resource management and operational efficiency. In India, IoT sensors are being used to monitor soil moisture levels, temperature and humidity. Additionally, IoT-enabled smart farming solutions are being implemented to automate irrigation systems, monitor livestock, and manage storage facilities.
Agricultural robots are used in smart agriculture to help with tasks such as crop monitoring, planting, and harvesting. They can also be used to apply pesticides and herbicides and to water plants. These robots can be controlled remotely or they can autonomously perform various repetitive and labour-intensive tasks, such as planting, weeding and harvesting. This can help address labour shortage and reduce the dependence on manual labour. Robotic systems can also be equipped with AI and ML capabilities to perform tasks such as selective harvesting, sorting produce and applying fertilisers or pesticides with precision. Robotic systems can work in harsh environmental conditions, making them well-suited for Indian agricultural settings. Some companies have developed autonomous drones equipped with sensors and cameras to monitor crop health, detect pest infestations and spray pesticides. These drones are capable of covering large areas of farmland efficiently and enable farmers to address crop health issues in a targeted and timely manner.
AI has the potential to significantly improve the agricultural industry by reducing environmental harm, improving yields and food quality and making processes more efficient. Implementing machine learning in agricultural processes is crucial for staying ahead of the competition. Those who can adopt these technologies will be well-positioned to reap the benefits. The applications of AI, ML and IoT in Indian agriculture are opening up new possibilities for digital solutions that address the evolving needs of the agricultural sector. By leveraging these technologies, farmers can gain access to valuable insights, automate labour-intensive tasks and take decisions to enhance productivity and sustainability.
Agricultural engineering
Agricultural engineering is playing a vital role in transforming the agricultural landscape and improving farmers’ income. Agricultural engineers develop technologies and processes for post-harvest handling, storage and processing of the produce. This reduces post-harvest losses due to spoilage and damage, allowing farmers to preserve the quality of their produce and access better market opportunities. By minimising waste and maintaining product quality, farmers can command higher prices and ultimately increase their income.
Through innovations in mechanisation, precision agriculture, irrigation systems, renewable energy, soil and water conservation, value-added processing and ICT solutions, agricultural engineers are contributing to increased productivity, efficiency and profitability in farming. By addressing the challenges faced by farmers and providing them with the tools and techniques to enhance their operations, agricultural engineering is significantly impacting the overall sustainability of agricultural systems.
The author is Head, Dept of Mechanical Engg, PAU, Ludhiana
Machine learning, the Internet of Things and artificial intelligence are opening up new possibilities for digital solutions that address the evolving needs of the agricultural sector. By leveraging these technologies, farmers can gain valuable insights, automate labour-intensive tasks and take decisions to enhance productivity. By addressing the challenges faced by farmers and providing them with tools and techniques to enhance their operations, agricultural engineering is impacting the overall sustainability of farming systems.
