Farmland multi-source information dynamic adjustment and fusion method and system
A multi-source information and dynamic adjustment technology, applied in the field of agricultural informatization, can solve problems such as misleading decision-making results, contradictory facts, and uncertain fusion results
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Embodiment 1
[0077] Such as figure 1 As shown, the method for dynamic adjustment and fusion of farmland multi-source information provided in this embodiment includes:
[0078] Step 101: Obtain multi-source farmland data and determine the multi-source data as evidence factors; the evidence factors include soil moisture, water stress index and stomatal conductance.
[0079] Step 102: Determine the recognition framework for data fusion; the recognition framework includes three propositions, namely irrigation proposition, non-irrigation proposition and uncertain proposition.
[0080] Step 103: Calculate the probability assignment values of each of the evidence factors to each proposition in the recognition framework, and establish a basic probability assignment matrix; the elements of the basic probability assignment matrix are probability assignment values.
[0081] Step 104: Calculate the conflict coefficient according to the basic probability distribution matrix and in combination with t...
Embodiment 2
[0115] Such as figure 2 As shown, a farmland multi-source information dynamic adjustment and fusion system provided in this embodiment includes:
[0116] The farmland multi-source data acquisition module 100 is configured to acquire farmland multi-source data and determine the farmland multi-source data as evidence factors; the evidence factors include soil moisture, water stress index and stomatal conductance.
[0117] The recognition frame determination module 200 is used to determine the recognition frame for data fusion; the recognition frame includes three propositions, namely irrigation proposition, non-irrigation proposition and uncertain proposition.
[0118] The basic probability assignment matrix building module 300 is used to calculate the probability assignment values of each of the evidence factors to each proposition in the recognition framework, and establish a basic probability assignment matrix; the elements of the basic probability assignment matrix are pr...
Embodiment 3
[0125] The present invention is carried out on the basis of monitoring information related to the crop itself and the growth environment collected by various types of sensors, including soil moisture sensors, soil temperature sensors, electrical conductivity sensors, wind speed and direction sensors, light intensity sensors, and light radiation sensors. As well as the real-time data of the canopy temperature sensor and stomatal conductance sensor for observing the growth of farmland crops, the above sensors collect data every 10 minutes.
[0126] Such as image 3 As shown, under the background of the above experimental environment, the specific steps of the method for dynamic adjustment and fusion of farmland multi-source information provided in this embodiment are as follows:
[0127] Step 1: Select multi-source farmland monitoring data as the evidence factor for data fusion, and determine a reasonable identification framework based on data characteristics and fusion decision...
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