A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

Ali Mirzazadeh,Iván Gallardo-Bernal,Yousef Abbaspour-Gilandeh,Mario Hernández-Hernández,José Luis Hernández-Hernández,Afshin Azizi

doi:10.3390/agronomy11112364

Abstract

Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops.

Highlights

Bird damage to agricultural and horticultural products leads to lots of problems, especially in high-value crops [1,2]
In order to more accurately evaluate the results of the deep learning-based models, a method based on conventional image processing (IP) was implemented
The other point is that for th1e0Roef s14Net50, the p-value of partially damaged class is less than 5% significance level which means that the classification of the images is sensitive to the class but not to other classes,ewd hrailpeefsoeredth),ewVhgigle16foarlgthoeriVthgmg1, 6thaelgpo-vriatlhume,otfhtehpe-pvaarlutiealolyf tdhaempaagretidalclylasdsaims alagregderctlahsasnisiglanrigfiecranthcaenlesvigeln,isfuicpapnocertilnevgetlh, astutphpeoirmtinagetshaamt tphlesimcoamge sfarommpldeisffceormenet fdriosmtridbuiftfieornesn.t distributions

Summary

Introduction

Bird damage to agricultural and horticultural products leads to lots of problems, especially in high-value crops [1,2]. Field research has indicated that the major damage to rapeseed crops in different regions of Iran, from the cultivation stage to the end of the rosette stage, is predominantly caused by migratory birds such as Tetrax tetrax and cuckoos [8]. A research group studied the activity of common cranes in arable fields as examples of large grazing birds, and their impacts on the agricultural sector [13] They surveyed different effective crop damage factors in order to develop preventive strategies and reported that proper approaches based on conservation practices are required to reduce crop damages. It is remarkable that the dataset was divided into 80% for training, 10% for validation, and 10% for test set, which numbered 296, 37 and 37 images, respectively

Performance Validation

Conventional Image Processing

Implementation Requirements

Findings

Conclusions