Deep Convolutional Neural Network for Chicken Diseases Detection

Hope Mbelwa,Jimmy Mbelwa,Dina Machuve

doi:10.14569/ijacsa.2021.0120295

Hope Mbelwa, Jimmy Mbelwa + Show 1 more

Open Access

https://doi.org/10.14569/ijacsa.2021.0120295

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Abstract

For many years in the society, farmers rely on experts to diagnose and detect chicken diseases. As a result, farmers lose many domesticated birds due to late diagnoses or lack of reliable experts. With the available tools from artificial intelligence and machine learning based on computer vision and image analysis, the most common diseases affecting chicken can be identified easily from the images of chicken droppings. In this study, we propose a deep learning solution based on Convolution Neural Networks (CNN) to predict whether the faeces of chicken belong to either of the three classes. We also leverage the use of pre-trained models and develop a solution for the same problem. Based on the comparison, we show that the model developed from the XceptionNet outperforms other models for all metrics used. The experimental results show the apparent gain of transfer learning (validation accuracy of 9􀀀% using pretraining over its contender 􀀁􀀂.􀀃􀀄% developed CNN from fully training on the same dataset). In general, the developed fully trained CNN comes second when compared with the other model. The results show that pre-trained XceptionNet method has overall performance and highest prediction accuracy, and can be suitable for chicken disease detection application.

Highlights

The poultry sector in Tanzania is economically significant, supporting up to 37 million households
We present the novel chicken disease detection method using Transfer Learning approach on a pre-trained Convolution Neural Networks (CNN)
The key elements that can improve the performance of the extension officers and poultry farmers in the early detection of chicken diseases are the use of computer-aided instruments and accurate data

Summary

Introduction

The poultry sector in Tanzania is economically significant, supporting up to 37 million households. The farmers in the country keep different birds, whereby chicken account for 96% of all livestock in the country [1], [2]. The growth rate of the poultry population is low, with an average of 2.6% annual growth in Tanzania mainland [1]. Production is greatly affected by different challenges like unreliable markets, scarce inputs [3], [4], shortage of timely extension information [5] and devastating diseases like Newcastle, Coccidiosis and Salmonella [6]. Coccidiosis is caused by parasites of the genus Eimeria that affects the intestinal tracts of poultry. Coccidiosis is ranked as a leading cause of death in poultry with Eimeria tenella (E.tenella) among the most pathogenic parasite [7]. The typical diagnostic procedure involves counting the number of oocysts (expressed as oocysts per gram [opg]) in the droppings or examining the intestinal tract to determine the lesion scores [8]

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