Automatic harvesting method for lodging crops and harvester

Abstract

The invention relates to an automatic harvesting method for lodging crops. The automatic harvesting method comprises the following steps of S100, acquiring an on-site crop image; S200, analyzing the on-site crop image, and judging whether the crops fall down or not; S300, generating an optimal control instruction according to a recognition result and transmitting the optimal control instruction toan execution component; S400, automatically adjusting corresponding parameters according to the control instruction and executing harvesting operation by the execution component; and S500, repeatingthe steps S100-S400 until the harvesting operation is finished. The invention further discloses an automatic harvester for the lodging crops. According to the automatic harvesting method and the automatic harvester, a lodging crop identification visual model is established by using a deep learning technology, so that the lodging condition of the crops on site can be accurately and automatically identified in real time, the computing power requirement is low, analysis can be completed on common terminal computing equipment, implementation is simple, only a camera device and a vehicle-mounted computing terminal need to be additionally arranged outside an agricultural machine to form an external module, and the agricultural machine is not changed at all.

Description

technical field [0001] The invention relates to the field of intelligent control of agricultural machinery (abbreviated as agricultural machinery), in particular to a method for automatically harvesting lodging crops and a harvester implementing the method. Background technique [0002] Intelligent driving and intelligent operation are the two core notable features of intelligent agricultural machinery. In recent years, intelligent driving technology with navigation and positioning as the core has been applied in agricultural machinery, but the technology related to intelligent operation has not yet been applied. As far as harvesting machinery is concerned, how Realizing the automatic harvesting of lodging crops is one of the key problems that intelligent agricultural machinery must solve. [0003] During the growth of food crops, due to the influence of natural environment such as strong wind / rain, crop yield such as ear weight, topdressing / watering and other factors, the c...

AI Technical Summary

Problems solved by technology

[0004] The current intelligent agricultural machinery generally avoids the problem of automatic harvesting of lodging crops. For example, some unmanned agricultural machinery handles: before harvesting crops, manually adjust the header and reel parameters in advance, and then the unmanned agricultural machinery The parameters are kept constant throughout the harvesting process, so the harvesting of lodging crops is obviously not accurate enough, resulting in a decrease in overall harvesting quality

There are also some methods: first use drones to take a large number of pictures in the work field, and then use image analysis technology, path planning technology, etc. to establish an operation map of intelligent agricultural machinery, and the area of ​​​​lodging crops is also marked on the map. Next, the intelligent agricultural machinery On-site operations are carried out according to the planned path according to the map. For the lodging crop area on the map, the background remote control of the front-end agricultural machinery completes the work. The disadvantage of this method is that the whole cycle is very long and the map cannot be constructed in real time. The reasons are: 1. Construction The map needs to collect a large number of pictures on the spot, such as tens of thousands; 2. It is necessary to model and analyze these large numbers of pictures, and the amount of calculation is very huge. Generally, a computer system with a large computing power is required to perform dozens of hours of calculations, which is obviously impossible. The above tasks are completed on the terminal or edge computing device on the drone, but with the development of time, the lodging crops are prone to large changes, resulting in inaccurate maps, so the harvest quality of lodging crops is naturally poor

Images