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Since the Indus Valley civilization, India's extremely changeable environment, extremes in temperature and rainfall (e.g. drought & flood) have prompted several advances in water management systems. This led to the gradual development of wells, pumps, irrigation systems, water storage systems, and novel, local water collecting methods. Even ancient Indian literature suggests that nature and its inherent processes may be understood intuitively.
Even now, several ancient rainwater gathering techniques are still in use, including kuls (Himachal Pradesh), johads and kunds (Rajasthan), Zabo (Nagaland), Eri (Tamil Nadu), Surangam (Karnataka), bamboo drip irrigation systems (Meghalaya), and Ahar pynes (Bihar). To maximize rainwater harvesting during India's intense monsoons, these ancient methods were carefully built depending on the climate, soil type, and land elevation.
Agriculture's use of water has recently changed due to the ongoing development of AI-based decision-support models and technologies. Ai in water management aids in identifying and analyzing when a plant is stressed owing to either limited or excess water supplies, even though one can never totally replace or imitate natural ecosystems. If none of these circumstances is remedied right away, plants will suffer and even die.
Artificial intelligence irrigation system models are trained to determine the ideal amount of water needed for a plant depending on several interconnected elements, including the plant's species, type, climate data, temperature, soil moisture, soil type, and irrigation system. The development of automated irrigation systems allows for the elimination of human approximation error when used in conjunction with AI models.
How can irrigation automation contribute to water conservation?
The artificial intelligence irrigation systems may be designed to detect dryness in the region and water the land when connected to the soil moisture sensors. In the following ways, this can help conserve water:
- When the plant needs water, it will simply irrigate the land.
- On the same plot of land, many soil types may exist, each requiring a different amount of water. It would be logistically impossible to manually irrigate these zones without over or under-irrigating them. A smart irrigation system will adjust its water flow based on the soil retention capabilities of each kind of soil in order to maintain the optimal moisture level.
- Cutting off the water flow as soon as the sensor registers an optimal value, will prevent overwatering. A common, undesired event caused by too much moisture in the soil is the leaching of soil nutrients, which may be readily avoided by such clever irrigation methods.
The development of artificial intelligence irrigation systems and their use in agricultural and water management
Agriculture has emerged as one of the sectors with the most potential for precision farming techniques and visibility tools as big data analytics and smart sensors have gained traction. Disruptive technology like AI has since brought about a revolution in agriculture. AI has defended crops against a number of threats, such as population increase, climate change, and issues with food security. It is also making progress in enhancing agricultural water efficiency.
AI in several irrigation system types
Automation in agriculture is increasingly built on AI. Simple and boring operations like acquiring field data, labeling and analyzing data, creating reports, and sending notifications are made easier with automation. In a short amount of time, the effects of these modest actions on agricultural practices will become apparent.
Less human effort, a unified understanding of soil properties, decreased water consumption, improved long-term landscape health, and cost savings are all aspects of smart irrigation. To accomplish these benefits, modern water irrigation systems make considerable use of IoT sensors and AI technologies.
- Center Pivot Irrigation: An AI-based system that controls sprinklers for circle irrigation and modifies the stream or angle of water flow and receives data insights from in-field sensors. This assists in reaching plants that are far from the water supply rather than overwatering the nearby ones.
- Sprinkler irrigation: To determine when to water next, an AI-based irrigation system gathers information from thermal and auditory rain sensors that gauge rainfall intensity. To avoid overwatering or excessive water usage, the system analyses and calculates this data before automatically alerting sprinklers.
- Water is supplied directly to the roots of the plant during drip irrigation and micro irrigation, which ensures consistent distribution, lowers evaporation and runoff, and increases irrigation effectiveness.
Data-driven water management decisions using AI analytics
Given that both overwatering and underwatering have the potential to harm crops or impede their growth, the quality of crops depends on the right level of irrigation. In addition, the soil has to be wet and the necessary humidity needs to be preserved. To attain the desired yield and quality, farmers must still make a judgment call over how much water to apply. However, it is a lengthy, operationally demanding, and complicated procedure that needs high-quality, localized data inputs.
AI can do much more than only increase agricultural productivity and cut production expenses. A new automated control for an irrigation system may be created using AI-based agriculture systems that leverage a variety of data sets, including satellite images, temperature, humidity, climate, and weather forecasts. This will help farmers make the best water management choices possible to use less water and save energy.
The creation of an irrigation system tailored to a particular crop has historically relied heavily on evapotranspiration, a water cycle that includes both evaporation and transpiration. Currently, without the requirement for site-specific calibration, sophisticated satellite imaging, weather forecasts, and remote sensing assist farmers in estimating and improving the evaluation of daily rainfall and potential evapotranspiration. More precise water predictions may also be made using data from weather sensors and a GIS-based system.
Conclusion
Industry disruption has lately been brought about by artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) sensors. These technologies offer a wide variety of capabilities, including enhancing human intellect and processing enormous amounts of data. There have been several conversations about how the new technology may assist in resolving water efficiency issues in this industry as AI, in particular, makes progress in agriculture to improve productivity and resource management.
The agriculture sector faces and contributes to water hazards due to climate change and the associated increase in catastrophic occurrences, such as floods and tropical storms, droughts, and diminished groundwater supplies.
