Below are discussed various advantages of using AI for Sustainable Agriculture. By utilizing AI technologies for agricultural production, farmers can increase productivity, maximize efficient resource utilization, and make more informed decisions for sustainable and efficient agriculture.
AI for Sustainable Agriculture (Crop Yield) by Biodiversity Protection:
Crop yield is improved by AI. One way this happens is through biodiversity conservation. Biodiversity functions to protect plants. Pollinating insects are essential for reproduction and are behind one out of every three bites that you eat. Insects also protect plant species from pest attacks. Biological pollinators are preferred over chemical pesticides. Image recognition algorithms in AI-powered drones give information about the insects. They can capture high resolution images of the fields and assist with the identification, quantification and location of insects. Using this data, conservationists can decide when its time to take action.
AI also helps protect habitats including the environments where these insects live. It uses satellites to photograph habitats and identify land usage patterns. AI can also make use of climate data and select which areas demand restoration or protection. Food and nesting sites are important resources for pollinators and AI contributes in the availability of such resources by alerting conservationists.
AI uses historical and real time data to build predictive models. It can predict the weather, changes in the landscape and the flowering cycles of plants. AI also foretells the potential threats such as loss of habitat or climate change. In order to protect these precious insects, AI educates the public through virtual assistants and chatbots. There are several pollinator friendly practices such as planting native flowers, minimizing chemical pesticide use and designing landscapes that favor pollinator survival.
Avoiding Pesticide Use:
In this regard, AI is useful as it identifies the exact spot where pesticide intervention is required so you don’t have to spray them all over. To accomplish this, AI employs satellite imaging, sensor networks, and pest traps. Using this approach, beneficial insects that would otherwise get killed are saved. AI creates predictive models to detect potential threats including not only pests but also weeds and disease allowing crop producers to take action right away. AI considers several variables such as pest presence, environmental conditions, and crop related factors. It then uses this detail to make decisions about when, where and how much to apply pesticides.
Integrated Pest Management is a concept which promotes the combination of several methods for restricting pests. Insect predators and novel plant varieties are brought into use. AI algorithms use different sources of data to recommend the best pest management strategy. These include weather conditions, pest populations, crop health and biological pesticides.
Irrigation Optimization via AI for Sustainable Agriculture:
Considering the fact that 85% of the world’s freshwater resources are spent on irrigation, it has become important to efficiently use water resources. Today the digitalizing agricultural world has welcomed new technologies that allow remote irrigation and optimal water usage. Sensors detect soil moisture levels, plant water requirements, humidity, and weather forecasts. This data is then used to optimize irrigation scheduling. The scheduling techniques save one from having to use manual methods that used soil water measurement.
Rain-drop sensors and soil moisture sensor installed in fields, lets the farmer know how much moisture the crop requires. Rain sensors are both thermal and acoustic. In the water on demand irrigation technique, a threshold is set according to the moisture content. Watering is allowed only when the moisture content is below the threshold. AI also alters the angle and speed of water flow from irrigation sources to ensure the water reaches the farthest plants. AI is capable of sending alerts when there is a leak or malfunction.
Crop Health Monitoring:
Using drones images, AI can identify plant stress indicators. These include nutrient deficiencies and other anomalies. Such images are high-resolution. Thus farmers can take action and make the environment more suitable for crop production. In addition to monitoring health, AI technologies enable you to make decisions regarding the exact amounts of fertilizer and pesticide application. This is possible due to the availability of data on soil conditions, plant health and yield maps.
AI creates customized presciptions for optimal input distribution. This reduces costs and also limits negative environmental impacts. Using such customized knowledge is a concept known as precision farming.
Crop Breeding and Genomics:
Next-generation sequencing allows identification of InDels and SNPs. Once you identify genes with unique qualities such as drought or pest resistance, you can select and breed plants with the best qualities. AI can accelerate crop breeding programs as it is capable of handling huge amounts of data.
To identify genetic markers that are relevant to desirable crop properties, machine learning algorithms are used. Today, attempts have been made to develop climate smart crops that will be able to survive even with the ever changing climate. This helps breeders make knowledgeable decisions about crop variety selection for breeding. Ultimately, improved crop varieties result.
Harvesting and Sorting:
AI algorithms can analyze historical and real-time data, including weather patterns, soil conditions, crop growth stages, and pest incidence, to predict crop yields. This information helps farmers make informed decisions regarding harvesting, storage, and marketing of their produce. This also helps farmers optimize their harvesting processes. AI using computer vision algorithms recognizes ripe crops. This technology also permits crop quality and assists crop sorting. This reduces the cost of labor and also improves efficiency.
