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The characteristic of peanuts, “flowering above ground and fruiting underground,” has always made it difficult for growers to grasp the development of underground fruit needles. Traditional methods rely solely on experience, often leading to issues such as soil compaction and improper moisture, which prevent the fruit needles from penetrating the soil, resulting in reduced yields. However, the emergence of AI root sensors has made the “invisible underground growth” visualizable, completely solving the “blind spot” problem in peanut cultivation.
This AI root sensor is only the size of a thumb and is made of corrosion-resistant nano-ceramic material. It can be buried 5-10 cm underground along with peanut seeds during planting. The sensor is equipped with a miniature camera, humidity sensor, and temperature sensor, capable of capturing real-time images of fruit needle growth while monitoring soil moisture, temperature, and compaction. It transmits data to a cloud platform via LoRa wireless communication technology. The AI algorithms in the cloud analyze the images and data: identifying the growth length and soil penetration angle of the fruit needles, determining whether the soil moisture is suitable (the optimal moisture for peanut fruit needle penetration is 60%-70%), and if the soil compaction exceeds 1.4g/cm³ (which is unfavorable for needle penetration), the system immediately pushes a “soil loosening suggestion” to guide farmers to use a small tiller for localized soil loosening.
In experimental fields in Shandong, a major peanut production area, after applying the AI root sensors, growers can clearly see the dynamic growth process of the fruit needles on a mobile app: from the formation of the fruit needles, downward growth, to soil penetration and expansion, each step is recorded with images and data annotations. When the soil penetration rate of the fruit needles falls below 80%, the system analyzes the reasons—if due to insufficient soil moisture, it automatically activates smart irrigation equipment to supplement water; if due to soil compaction, it reminds farmers to loosen the soil in a timely manner. Ultimately, the soil penetration rate of peanut fruit needles in the experimental field increased from 75% in traditional planting to 92%, with an average yield increase of 18% and a 12% reduction in empty pods.
More importantly, the AI algorithm can establish a predictive model of “soil conditions – fruit needle growth” through years of data accumulation. For example, when the soil temperature stabilizes at 22°C and the humidity at 65%, the system can predict the soil penetration time of the fruit needles to be about 5-7 days, helping growers plan management measures in advance. This model of “underground visualization + intelligent decision-making” not only solves traditional problems in peanut cultivation but also provides a replicable technical solution for monitoring other underground fruiting crops (such as potatoes and sweet potatoes).
