At present, machine learning and decision making into an interconnected system, farming practices would amend into with knowledge-based agriculture that would be capable to proliferation production levels and products quality.

  • Agriculture Robot: Most of the enterprises are now programming and designing robots to handle the vital task associated to agriculture. This comprises spraying of pesticides and insecticides, harvesting of crops and more efficient work than human employees.
  • Monitor crop and soil: Firms are now making use of technologies and deep learning algorithms. The data are gathered via drones and other software to observe crops and soil. They also practice the software to control the fertility of the soil.
  • Water Management: Water management in agriculture influences hydrological, climatological and agronomical equilibrium. So far, the most established ML-based applications are associated with approximation of daily, weekly or monthly evapotranspiration allowing for a further effective use of irrigation systems and forecast of daily dew point temperature, which benefits in detecting expected weather phenomena and estimated evapotranspiration and evaporation.
  • Disease Detection: Both in open-air and greenhouse circumstances, the most extensively used practice in pest and disease control is to consistently spray pesticides over the cropping area. To be effective, this method needs substantial amount of pesticides which results in a high monetary and significant price. ML is used as a portion of the general precision agriculture administration, where agro-chemicals involvement is targeted in terms of time, place and affected plants.
  • Weed Detection: Apart from illnesses, weeds are the most significant threat to crop production. The main and challenging problem is detection of weeds from crops.  Computer Vision and ML algorithms can improve finding and discrimination of weeds at little costs and with no environmental problems and side effects [6].
  • Crop Quality: The accurate decision and classification of crop quality features can intensify product price and diminish waste. In comparison with the human experts, machines can make use of apparently meaningless data and play role in the overall quality of the crops.
  • To boost the yield of crops: The AI technologies are used to decide which crop and at which conditions will harvest the best yield. It will also determine which weather condition will provide the maximum profit.
  • Retailers: The seed retailers use this technology to blend the data to create better crops. While the pest control firms are using them to diagnose the various bacteria’s and bugs.

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