# Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features

## TL;DR

This paper introduces a novel DCNN-based approach augmented with handcrafted texture features for accurate, real-time lodging prediction in wheat and canola crops using UAV imagery, improving efficiency in crop breeding programs.

## Contribution

The study presents a new DCNN architecture combined with handcrafted features that outperforms existing methods and is suitable for real-time high-throughput phenotyping.

## Key findings

- Proposed model surpasses state-of-the-art lodging detection methods.
- Achieves comparable accuracy with fewer parameters than other DCNN models.
- Enables real-time lodging classification on inexpensive hardware.

## Abstract

Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at https://github.com/FarhadMaleki/LodgedNet contains code and models.