Research on the Design Method of Blueberry Automatic Harvesting Clamp Force Based on Neural Networks

Abstract

Highlights The relationship between fruit size and gripping force was found by BP algorithm. The error between the optimized holding force and the optimal value is not more than 8%, that is, the fruit integrity rate is greater than 92%. The simplified two-parameter method is more suitable for efficient field operation. Abstract. The flourishing development of the blueberry industry has urgently demanded mechanized harvesting, and the rational design of the clamping force of mechanical claws is a challenging aspect for achieving automatic and flexible harvesting. This article proposes a method to predict fruit hardness based on blueberry shape parameters, enabling dynamic adjustment of clamping force. Fruit diameter, height, weight, and rupture pressure data were collected, and using the classical BP neural network algorithm, a mapping relationship between fruit hardness and shape parameters was established. Experimental verification was conducted using an orthogonal table, and the results indicate that the predicted blueberry fruit hardness using this method deviates by less than 8% from the actual values. Further simplification into a two-parameter method was performed to enhance practical field operation efficiency. Although the error slightly increased, it still met the requirements of actual field operations.