Farmland soil fertility evaluation method based on convolutional neural network

Abstract

The invention discloses a farmland soil fertility evaluation method based on a convolutional neural network. The method comprises the steps: obtaining an original farmland image, and building a farmland soil fertility classification model; performing normalization processing on each original farmland image, and performing data expansion on the normalized original data to form a training set; training the farmland soil fertility classification model by adopting the training set, obtaining a plurality of images of the target farmland as to-be-evaluated pictures, inputting the to-be-evaluated pictures into the trained farmland soil fertility classification model, and outputting a corresponding classification result; and obtaining the fertility evaluation grade of the target farmland after statistical analysis. According to the method, samples are obtained under the condition of not damaging farmland and crop growth, the soil fertility condition of the current land parcel is repeatedly calculated through deep learning, manpower and material resources are saved, and the classification accuracy is high; meanwhile, by combining a soil fertility multi-parameter space-time distribution rule, effective technical support is provided for accurate fertilization in final intelligent agriculture.

Description

technical field [0001] The invention relates to the technical field of soil fertility evaluation, in particular to a method for evaluating farmland soil fertility based on a convolutional neural network. Background technique [0002] The traditional observation of crop growth status requires artificial on-site observation, which has a large workload and is easy to damage the farmland. my country is the most populous country in the world. The agricultural production mode from ancient times to the present is mainly a traditional agricultural production and management labor mode based on small-scale labor. However, the traditional labor model has low agricultural productivity, small scale, and low degree of organization, which leads to the low labor rate and output of my country's current agricultural production, and the current low efficiency of agricultural resource utilization in my country, which further aggravates the deterioration of the ecological and natural environment...

AI Technical Summary

Problems solved by technology

The current evaluation of soil fertility is achieved by collecting a large number of soil samples in the farmland, and then analyzing the physical and chemical properties of the soil in the laboratory; in this method, the results of different samples vary greatly, and each sample can only reflect a small area. The soil properties of the block lack a global characterization; more importantly, the current evaluation methods are time-consuming and labor-intensive, requiring a lot of labor and time, and low efficiency

In addition, there are many subjective and objective factors in the evaluation of soil fertility, and the comprehensive evaluation of soil fertility still needs to be improved and perfected.

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