Multi-Agent Vision System for Supporting Autonomous Orchard Spraying

Abstract

In this article, the authors propose a multi-agent vision system supporting the autonomous spraying of orchards and analyze the condition of trees and occurrence of pests and diseases. The vision system consists of several agents: first, for the detection of pests and diseases of fruit crops; second, for the estimation of the height of trees to be covered with spraying; third, for the classification of the developmental status of trees; and fourth, for the classification of tree infections by orchard diseases. For the classification, modified deep convolutional neural networks were used: Xception and NasNetLarge. They were trained using transfer learning and several additional techniques to avoid overfitting. Efficiency tests performed on the datasets with real orchard photos, showing accuracies ranging from 96.88% to 100%. The presented solutions will be used as part of an intelligent autonomous vehicle for orchard works, in order to minimize harm to the environment and reduce the consumption of water and plant protection products.