Method for early-prediction of fruit tree yield

Abstract

The invention provides a method for early-prediction of the fruit tree yield. The method comprises the steps that S1, according to the requirement for fruit ranch management, the image obtaining time is determined and images of a fruit tree which is ready to be predicated in yield are collected by a portable image collecting device in a certain time under a certain image collecting condition; S2, fruit zone identification is conducted according to image characteristics; S3, leaf zone identification is conducted according to the image characteristics; S4, characteristics of a fruit zone and a leaf zone are extracted to serve as fruit tree crown characteristics; S5, the fruit tree crown characteristics are input into an artificial neural network yield measuring model for predicting the yield of the fruit tree. According to the method, the image processing and identification technology is effectively combined with the artificial intelligence technology, the defect that only the image processing and identification technology is used for prediction is overcome, and therefore prediction can be accurately conducted on apples in the fruit ranch in an early period.

Description

technical field [0001] The invention relates to the technical field of agricultural forecasting, in particular to a method for early forecasting of fruit tree yield. Background technique [0002] Apple is one of the most widely eaten fruits in the world, and the world's annual apple production is about 32 million tons. China is the world's largest apple producer and consumer, with apple planting area and output accounting for more than 40% of the world's total, occupying an important position in the world's apple industry. In order to estimate the apple yield before harvesting and to arrange various manpower and material resources required for harvesting, the current common method is to manually extract some fruit trees, count the number of apples one by one, and then roughly estimate the yield. This manual method is time-consuming, laborious and inaccurate. In the study of early forecasting of apple yield, computer information processing technology as a means has become o...

AI Technical Summary

Problems solved by technology

However, it is a big challenge to rely solely on image processing technology to predict the yield by accurately identifying apples on the tree, and to solve the problems of overlapping apples and occlusion of apples by leaves.

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