In the 19th century, machines were deployed as a substitution for human labour during the Industrial Revolution. Over time, with the advancements in information technology in the 20th century, post the arrival of computers, the vision for artificial intelligence (AI) powered machines initiated.

Novel technologies have revolutionised farming practices. Introducing AI solves several hurdles and aids in diminishing many disadvantages of traditional farming. Here, let's look at AI tools that can help Indian farmers overcome challenges and improve productivity.

Smart irrigation: A smart Irrigation system is an Internet of Things (IoT)- based device that can automate the irrigation process by analysing soil moisture and climate conditions. Automated irrigation systems are designed to utilise real-time machines that can constantly maintain desired soil conditions to increase average yields. This significantly reduces farmers' struggles and provides the potential to drive down production costs. 

Crop and soil health monitoring systems: Using the image recognition approach, AI accurately identifies defects in images captured by the camera. Such AI-enabled applications are very supportive of understanding soil defects, plant pests, plant stress, and diseases. Remote sensing (RS) techniques, along with hyperspectral imaging and 3D laser scanning, are effective in constructing a crop matrix over thousands of acres of cultivable land. 

Decrease pesticide usage: The AI collects data to check the weed infestation area, which helps the farmers spray chemicals only where the weeds are. This reduces pesticide and herbicide losses.

Disease detection: A fuzzy logic-based model has been developed to forecast diseases based on leaf wetness duration. Pre-processing of image segment of the leaf image into areas like background, non-diseased, and diseased parts. Then, cropped diseased parts are sent to remote labs for further diagnosis. Pest identification and nutrient deficiency recognition can also be done by image processing.

Drones and Unmanned areal vehicles (UAV): Drones can be leveraged to produce a 3-D field map of detailed terrain, drainage, soil viability and irrigation before the crop cycle. Necessary plant supplements can be provided by aerial spraying of pods along with seeds and plant nutrients into the soil. High-resolution cameras in drones collect precision field images, which can be passed through a convolution neural network to identify areas with weeds, which crop water needs, and plant stress levels in the mid-growth stage. Effective management of soil N2 levels can be achieved by drone. Depending on the terrain, drones can be programmed to atomise liquids by regulating the distance from the ground surface.

Soil analysis: With the help of sensors, cameras, and infrared rays that scan the soil for its nutritional properties, AI can help in understanding the reaction of specific seeds to different soils. It can analyse the impact of weather changes on the soil, and also the probability of the spread of diseases and pests.

Precision farming: Precision farming is a technique with high accuracy and control capacity. It substitutes the repetitive and labour-intensive part of farming and guides crop rotation. The advanced technologies that enable precision farming are high-precision positioning systems, geological mapping, remote sensing, integrated electronic communication, variable rate technology, optimum planting and harvesting time estimators, water resource management, plant and soil nutrient management, and attacks by pests and rodents.

Chatbots: In agriculture, chatbots can be used for communication between farmers, government stakeholders, manufacturers, and markets. Agriculture could also grasp this emerging technology by assisting farmers with answers to their questions and giving advice and recommendations on specific farm problems.

Driverless Tractors: Robotic agriculture is a technology that is slowly gaining momentum. Driverless tractors are independent tractors that perform all farm practices autonomously and precisely. They are fixed with sensors that can perform the required practices, monitor obstacles, and determine where to apply the farm inputs.

Greenhouse automation: Many factors influence plant growth and the ripening of produce in greenhouses, which can be impossible for humans to analyse. AI analyses all these growth factors possible and provides highly accurate assessments of plant growth.

Weather forecasting: A variety of sensors, satellites and computer models are utilised by meteorologists to predict future weather patterns. Reinforcement learning is applied by AI techniques that consider past predictions and actual outcomes. Deep learning techniques have already been successful in areas like image recognition, speech recognition and natural language processing (NLP), and they can also be applied to the weather and climate fields.

Introduction of AI can seem a difficult task. But in the long run, it can turn out to be cost effective, and provides vast employment opportunities.

Content: Research paper by ICAR-Central Arid Zone Research Institute.

Image Source: Unsplash