• To have devices thatto collect and synthesize in-field climate and plant data to produce actionable insights in all growing conditions.
• The solution uses a robust sensor suite with, rugged durability, and cutting-edge global cellular connectivity.
• Machine learning is used to provide deeper insight to climate variability, crop health, etc. by tracking more than 40 parameters.
• User gets deep visibility into climate variability, and crop health, which they can factor while taking decisions about event timing and irrigation.
• As per the company’s website, a comparison between Arable’s in-field weather and standard gridded weather shows that Arable is 350% more accurate in the critical frost range that results in crop damage.
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