Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths

Chiwamba, Simon H.; Phiri, Jackson; Nkunika, Philip Obed Yobe; Nyirenda, Mayumbo; Kabemba, Monica M.; Sohati, Philemon H.

doi:10.33260/zictjournal.v3i1.69

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…15 shows the details of a single record on a webpage uploaded to the cloud server. The ML attribute (prediction accuracy) is based on the Googles pre-trained InceptionV3 Machine Learning model adopted by Chiwamba et al (2019) and Chulu et al (2019b). The model achieved a 90% plus prediction accuracy for all images that contained a FAW moth as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The IoT technologies include a Raspberry Pi 3 Model B+ micro-computer, Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors which include the pi camera, DHT11 temperature/humidity, Davis anemometer, powered with an off-grid solar photovoltaic system for capturing FAW images and environmental conditions in the field. This work is a build-up on the preliminary works that were published by Chiwamba et al (2019; and Chulu et al (2019a;2019b).…”

Section: Introductionmentioning

confidence: 99%

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Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Chiwamba¹,

Phiri²,

Nkunika³

et al. 2019

Journal of Computer Science

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Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of infestation. In addition, the insect pest's abundance and environmental conditions should be predicted as early as possible for integrated pest management to be effective. Consequently, a fall armyworm pheromone trap was deployed as a monitoring tool in the present study. The trap inspection is currently carried out manually every week. The purpose of this paper is to bring automation to the trap. We modify the trap and integrate Internet of Things technologies which include a Raspberry Pi 3 Model B+ microcomputer , Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors powered with an off-grid solar photovoltaic system to capture real-time fall armyworm moth images, environmental conditions and provide real-time indications of the pest occurrences. The environmental conditions include Geographical Positioning System coordinates, temperature, humidity, wind speed and direction. The captured images together with environmental conditions are uploaded to the cloud server where the image is classified instantly using Google's pre-trained InceptionV3 Machine Learning model. Intended users view captured data including prediction accuracy via a web application. Once this smart technology is adopted, the labour-intensive task of monitoring will reduce while stakeholders shall be provided with a near real-time insight into the FAW situation in the field therefore enabling pro-activeness in their management of such a devastating pest.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Chiwamba¹,

Phiri²,

Nkunika³

et al. 2019

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

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“…S. Chiwamba, J. Phiri, P. Obed et al [9], [10], use convolutional Neural Network machine learning algorithm in the automated identification and capturing of fall armyworm moths. The achieved train accuracy was 45-60%, cross entropy was 70-80% and validation accuracy was 34-50%.…”

Section: Figure 1: Dataset Descriptionmentioning

confidence: 99%

Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

Maambo¹,

Phiri²,

Kalumbilo³

et al. 2022

zictjournal

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In recent years increase of new and effective medical field applications has critical part in research. Artificial Intelligence (AI) Systems has great influence in the growth of these effective medical field applications and tools. One of the major health problems in both established and developing countries is heart disease. Therefore,diagnosis to regulate the heart disease is very vital, so that appropriate actions can be taken. The Artificial Intelligence System uses input medical data collected from an existing dataset from Kaggle and applies this data on the artificial intelligence application developed that uses data mining algorithm and a basic model on Zambian patients to see if the model will predict correctly. From the dataset collected 80% was used as training data and 20% was used as testing data. The Bayesian data mining algorithm was used for predicting the risk level and probability of heart disease. The system uses medical parameters to predict heart disease in patients and these parameters are age, sex, blood pressure, blood sugar (mg/dl), cholesterol (mm/dl),heart rate, exercise-induced angina, resting electrocardiogram, oldpeak, ST-slope and chest pain type. The data set collected by the system went through preprocessing which later supervised learning techniques and prediction model was conducted. Results were produced. Based on the results with the prediction accuracy of 90.97%, our results are in the same range as generated by other algorithms like KNN, Random Forest and Decision Tree algorithm.

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“…Artificial intelligence (AI) has been used by scholars elsewhere to predict rainfall [3], diseases [4,5], financial fraud [6] and many phenomena. This study aims to use AI to analyse the sentiments that were expressed online in relation to the 12 th August Zambia general elections.…”

Section: Introductionmentioning

confidence: 99%

Towards Election Forecasting Using Sentiment Analysis: The Zambia General Elections 2021

Ayami

Nyirenda

2023

zictjournal

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Forecasting of election results is one of the key activities prior to elections. In Zambia, like many other countries, opinion polls have been used to predict the outcome of elections since 1999. During the run up to the 2021 general elections, two opinion polls were conducted. One poll suggested that HH would emerge victorious whilst the other predicted that ECL would emerge victorious. Actual results announced on the 16th of August 2021 by the Electoral Commission of Zambia (ECZ) had HH obtaining 59.02% of the votes. The variance in the two opinion polls leaves room for alternative approaches to predicting election results. This study proposes sentiment analysis as part of the initial stage to building an alternative solution to predicting the outcome of an election. The study analysed sentiments shared on social media during the build up to the August 2021 general elections. A total of 3,519 tweets were scrapped from Twitter and sentiment analysis was performed on the tweets. Topic modeling was subsequently also performed on the tweets using BERTopic. The findings of the study reveal that as election day drew closer, there was an exponential increase in the number that were posted on a daily basis. Some of the topics included voter engagement and education, the shutdown of the internet and the election day.

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Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths

Cited by 7 publications

References 4 publications

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

Towards Election Forecasting Using Sentiment Analysis: The Zambia General Elections 2021

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