Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network.

ABSTRACT:

Introduction

Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level.

Methods

This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the 'Independence' cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (

SUBMITTER: Tang M 

PROVIDER: S-EPMC9975588 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature