Application of Artificial Intelligence and Machine Learning in Agriculture
The study of technologies and tools that are used to do activities that call for human intelligence is known as artificial intelligence (AI). tasks include visual perception, decision-making, processing, creation, and interpreting natural language, among many more. The two most popular AI techniques are machine learning and deep learning. AI has revolutionized agriculture and other facets of life with its cutting-edge innovations. The low expert to farmer ratio necessitates AI interventions like computerized diagnosis and recommendation of appropriate recommendations, as over 50% of the workforce is working in agriculture. Crop production decision-making, disease and pest infestation, weather forecasting, yield prediction, advising systems for increased crop productivity, etc. are the main challenges in agricultural production. The agriculture industry is under tremendous pressure to boost crop output and optimize yields due to the world's population expansion, which is expected to exceed 10 billion people by 2050. Two possible strategies have surfaced to address impending food shortages: embracing creative methods and utilizing technology improvements to increase productivity on existing farmland, or extending land use and implementing large-scale farming. The contemporary agricultural landscape is changing and taking many creative turns as a result of numerous challenges to reaching targeted farming production, including shrinking soil fertility, labor shortages, climate change, environmental problems, and limited land holdings. Certainly, farming has advanced significantly since the days of hand plows and horse-drawn equipment. New technologies are introduced every season to increase productivity.
In order to do their tasks with substantial precision, machine learning does actually benefit from vast amounts of data. Obtaining large and varied data in the agricultural setting can occasionally be difficult, but it is essential for the effectiveness of machine learning models. Because IoT sensors may be strategically placed throughout fields to gather pertinent data on, for example, soil conditions, climate variables, crop health, and livestock metrics, they are essential for gathering a wide variety of agricultural data.