Amongst the many issues hurting farmers, three stand out due to their global presence and financial impact:
Pests: Pests devour approximately 40% of global agricultural productivity annually, costing at least $70 billion. From locust swarms decimating fields in Africa to fruit flies affecting orchards, the impact is global, and financial repercussions are colossal.
Soil Quality and Irrigation: Soil degradation affects nearly 33%of the Earth's soil, diminishing its ability to grow crops, leading to a loss of about $400 billion. Water scarcity and inefficient irrigation further dent agricultural output. Agriculture uses 70% of the world's accessible freshwater, but 60% of it is wasted due to leaky irrigation systems.
Weeds: Despite advancements in agricultural practices, weeds cause significant declines in crop yield and quality. Around 1800 weed species reduce plant production by about 31.5%, leading to economic losses of about $32 billion annually.
Artificial Intelligence is often used as a catchall phrase. Here, it refers to the systematic collection of data, pertinent use of analytics ranging from simple descriptive summaries to deep learning algorithms, and advanced technologies such as computer vision, the internet of things, and geospatial analytics.
Pest identification and control: Accurate, early identification and control of pests is essential to minimize crop damage and reduce the reliance on chemical pesticides. Data such as weather reports, historical pest activity, and high-resolution images captured by drones or satellites are readily available today.
Soil health monitoring: Continuous monitoring and analysis of soil health are essential to ensuring optimal growing conditions and sustainable farming practices. Optimizing water use is crucial to ensuring crops receive precisely what they need, reducing waste and enhancing productivity.
Weed Detection and Management: Precise identification and elimination of weeds is critical to preventing them from competing for precious resources with crops and minimizing herbicide use. Thanks to computer vision, drones and robots can now identify weeds amongst crops with high precision.
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