A precision agriculture management platform and method based on big data technology

Abstract

The invention relates to a precision agricultural management platform and method based on big data technology, which is characterized in that a data acquisition end, a storage platform, a central server, an execution device, a human-computer interaction device and a transmission network are provided. The storage platform adopts the combination of memory and cloud data platform, the memory platformreceives data streams output from the data acquisition terminal, the central server, the execution device and the human-computer interaction device, the central server is provided with an arithmeticunit and a controller for generating control instructions, the controller realizes the control of the execution device, the human-computer interaction device, the data acquisition terminal, the arithmetic unit, the storage platform and the transmission network itself through the transmission network. The instructions and data in the storage platform are stored in the memory of the storage platformin the same storage mode and transmitted to the controller through the instruction stream. The invention can realize data collection of crop growth state, growth environment, soil moisture and the like, and provide sufficient data accumulation for the follow-up.

Description

Technical field: [0001] The invention relates to the technical field of intelligent agricultural equipment, in particular to a precision agricultural management platform and method based on big data technology. Background technique: [0002] At present, there are mainly the following problems in the production of agriculture, forestry, garden care and other industries involving plant cultivation (for the sake of simplicity in the following, only "agriculture" is used as a typical example): (1) Serious dependence on experience: managers The management of farmland is carried out according to their own experience in agriculture, so the level of management strongly depends on the experience of each manager. And due to the limitation of everyone's knowledge level, it is difficult to achieve comprehensive and efficient management. (2) No quantitative indicators: It is difficult to realize real-time detection and accurate analysis of crop growth environment and crop demand. (3) N...

AI Technical Summary

Problems solved by technology

[0002] At present, there are mainly the following problems in the production of agriculture, forestry, garden care and other industries involving plant cultivation (for the sake of simplicity in the following, only "agriculture" is used as a typical example): (1) Serious dependence on experience: managers The management of the farmland is carried out based on its own experience in agriculture, so the level of management is strongly dependent on the experience of each manager

And due to the limitation of everyone's knowledge level, it is difficult to achieve comprehensive and efficient management

(2) No quantitative indicators: it is difficult to achieve real-time detection, and it is difficult to achieve accurate analysis of crop growth environment and crop demand

Indiscriminate management will result in an imbalance between plant demand and human intervention (giving), and the maximum growth potential of plants cannot be exerted, and high economic and ecological benefits cannot be achieved.

(4) Waste of resources and pollution of the environment: Due to the limitation of the knowledge level of managers and the lack of quantification of crop demand, it is impossible to scientifically and rationally use fertilizers, pesticides, etc., which is very easy to cause mistakes and excessive use of them, which can neither be guaranteed The healthy growth of crops causes waste of resources and environmental pollution

(5) Low implementation precision: When implementing fertilization, spraying and other operations, manual methods will lead to low implementation accuracy, not only in the inability to accurately prepare the required fertilizer ratio and drug ratio, but also in the inability to achieve uniformity, high efficiency and Accurate application or spraying on crops

(6) No prediction ability: mainly adopt "remedial" methods, such as spraying pesticides after pests and diseases, but cannot realize early warning and prevention of crop growth environment, diseases, pests and diseases, etc.

(7) Low efficiency, time-consuming and labor-intensive: At present, these industries rely heavily on manpower and machinery with low intelligence, resulting in low production efficiency, time-consuming and labor-intensive

At present, the key difficulties of precision agriculture are concentrated in three aspects: 1. Inability to monitor comprehensively, efficiently and in real time: At present, it is difficult to obtain data on environmental factors of plant growth. On the one hand, the existing farmland environmental monitoring system has not formed a clear On the other hand, the types of detection modules are limited, real-time monitoring is difficult, and the amount of data obtained is limited

2. It is difficult to formulate a decision-making model: (1) It is difficult to accurately estimate the plant growth demand (yield), and there is a complex nonlinear relationship between the yield and various environmental factors of plant growth (including temperature, humidity, soil nutrients and other factors) , the existing precision agriculture schemes face the dilemma of making decision-making models difficult

(2) As the amount of detectable data increases, the difficulty of data analysis increases sharply, and the formulation of decision-making models becomes increasingly difficult

(3) Existing decision-making models mostly adopt pre-set methods, and the model remains unchanged for a long time without self-learning ability

3. Difficulties in implementing decisions:

[0005] (1) At present, most precision agriculture platforms only focus on environmental monitoring, manual analysis, and then manually perform follow-up management operations

Due to human participation, it is time-consuming, labor-intensive, inefficient, and due to the influence of subjective factors, there is a large error in the implementation of decision-making

(2) A small number of precision agriculture platforms can realize more complex functions such as irrigation (drip irrigation), but on the one hand, there are very few operations that can be performed, and the functions are single; on the other hand, the degree of precision is low, such as using large-scale sprinkler irrigation. , without discrimination

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