The invention provides a self-powered
crop growth
condition monitoring system based on
machine learning. The self-powered
crop growth
condition monitoring system comprises a self-powered module, a weight sensing module, an intelligent analysis module, a user monitoring terminal, an intelligent
irrigation module and an intelligent fertilization module. And the intelligent analysis module comprises processes of
data acquisition, feature labeling,
feature extraction, model training and state judgment. The weight sensing module converts
LED lamp position changes caused by
crop growth into weight data through the elastic
deformation mechanism and the optical marking
assembly, a traditional sensor is not needed, cost is low, and potential safety hazards of a battery are eliminated. According to the
system,
drip irrigation water
drop impact energy is collected through a self-powered module (TENG technology) to achieve autonomous power supply, weight data and a
training set are compared and analyzed by combining a
machine learning model, and high-precision monitoring of the health state of a
single plant is achieved. The user monitoring terminal integrates data through
the Internet of Things and visually displays the data, when diseases are detected, the intelligent
irrigation and fertilization module is automatically controlled to stop water and
fertilizer supply, the agricultural production accuracy and efficiency are improved, and the problems that in the prior art, real-time performance is poor, precision is insufficient, and power supply depends are solved.