How AI/ML technologies are increasing agricultural productivity and profitability

Original article can be found here (source): Artificial Intelligence on Medium

How AI/ML technologies are increasing agricultural productivity and profitability

The Covid19 pandemic has already had a damaging effect on agriculture and allied sectors across the globe. While local ecosystems have encountered severe disruption, global supply chains have completely crashed. The crisis will soon pass but one of its most critical impacts will be — firstly, faster adoption of digital technologies and secondly, increased mechanization across the value chains. This is where data science combined with artificial intelligence and machine learning (AI/ML) will come increasingly into play. The whole concept of smart farming, which is making agriculture more efficient and sustainable, and thus profitable, is largely driven by AI/ML technologies. These technologies can be used in crop and water management, pest and disease detection, crop health monitoring and yield estimation, cultivating and harvesting by smart tractors without drivers as well as other types of forecasts and predictive analytics. These technologies combined with others like remote sensing and big data bring about data-intensive processes in agriculture, which increases the efficiency and productivity of agriculture at a time when it has become imperative to produce more with less. Here are a few ways in which AI/ML can boost agricultural productivity. A key application of AI has been helping in identifying pests and diseases. Custom databases for specific crops and helps farmers identify pests and plant diseases with nothing but just a mobile phone. This saves human intervention, cost of hiring an expert and, most importantly, there is no delay in diagnosis. Sensors are also being used to detect and target weeds. In some instances, robots are used to uproot weeds and in others, it helps in targeted application of pesticides. One research team that used AI technology to detect disease in cassava plants in Tanzania found that AI was able to detect disease with 98 percent accuracy. Instead of spraying pesticides uniformly over the entire cropping area which is an expensive proposition for the farmer, ML can aid in targeting the inputs precisely in terms of time, place and affected plants. This can reduce the chemicals used and improve the quality of produce, and save cost. AI/ML is playing a significant role in advancing hyper-local weather predictions. Using massive data coming from weather satellites combined with continuously expanding weather stations and IoT sensors on the ground, more accurate hyper localized weather predictions are becoming possible. Some models go as granular as 4km resolution. This type of hyper-local weather data is increasingly utilized to provide targeted advisories in a given cluster of villages.

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