Browsing by Author "Danison, Taremwa"

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Classification of cassava leaf diseases using deep Gaussian transfer learning model
(Engineering Reports, 2023-03) Emmanuel, Ahishakiye; Ronald, Waweru Mwangi; Petronilla, Murithi; Fredrick, Kanobe; Danison, Taremwa
In Sub-Saharan Africa, experts visually examine the plants and look for disease symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine learning algorithms have been employed to quickly identify and classify crop diseases. In this study, we propose a model that integrates a transfer learning approach with a deep Gaussian convolutional neural network model. In this study, two pre-trained transfer learning models were used, that is, Mobile Net V2 and VGG16, together with three different kernels: a hybrid kernel (a product of a squared exponential kernel and a rational quadratic kernel), a squared expo-nential kernel, and a rational quadratic kernel. In experiments using MobileNet V2 and the three kernels, the hybrid kernel performed better, with an accuracy of 90.11%, compared to 86.03% and 85.14% for the squared exponential kernel and a rational quadratic kernel, respectively. Additionally, experiments using VGG16 and the three kernels showed that the hybrid kernel performed better, with an accuracy of 88.63%, compared to the squared exponential kernel’s accuracy of 84.62% and the rational quadratic kernel’s accuracy of 83.95%, respectively. All the experiments were done using a traditional computer with no access to GPU and this was the major limitation of the study.
A comparative study of some pre-trained models on transfer learning approach in detection and classification of cassava leaf diseases
(SSRN, 2022) Emmanuel, Ahishakiye; Waweru, Mwangi; Petronilla, Muthoni; Danison, Taremwa; Fredrick, Kanobe
Cassava diseases affect cassava harvest posing the greatest danger to the food security and livelihoods of more than 200 million people. To identify cassava diseases, government professionals visit various sections of the country and visually score the plants by looking for disease indicators on the leaves. This procedure is notoriously subjective; it is not uncommon for specialists to differ on a plant's diagnosis. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. Motivated by the current developments and many influential studies in the field of deep learning and transfer learning models in the detection and classification of crop diseases, this study evaluates the performance of VGG16, VGG19, ResNet50, InceptionV3, DenseNet201, and MobileNetV2 in detection and classification of cassava leaf diseases. Fine-tuning of the hyperparameters was done during training to improve the accuracy of the models. Experimental results on the cassava dataset revealed that InceptionV3, DenseNet201, and MobileNetV2 models had high training accuracy but low validation accuracy with various epochs which means that they had issues with over fitting while ResNet50 had issues with underfitting. Moreso, VGG16 and VGG19 models performed well on both training and validation datasets, though VGG16 performed relatively well compared to VGG19.
A secure web based records management system for prisons: a case of Kisoro prison in Uganda
(International journal of computer, 2017) Emmanuel, Ahishakiye; Danison, Taremwa; Elisha, Opiyo Omulo
Most Prisons in the developing countries are still using the traditional system – pen and papers, to keep track of their records. This system takes long to finish a single transaction; this has led to loss of information of some cases (crimes files), insecurity and data redundancy. Similarly, some cases have been reported where some prison staff connives with clients (victims) to change and hide some information or files hence leading to compromising the evidence of the matter. This has consequently resulted in time wastage to handle cases, increased corruption and insecurity of important files hence making the whole process costly. Also when reports are needed especially about prisoners, it takes a long time and therefore makes it hard for Prison Management to take urgent decisions. This has created a lot of loopholes in the system because there is no tracking and/or monitoring of the information available in the different Departments and there are no security measures in place to safe guard the available information. This necessitated automating the system to make it more efficient and effective. There was close study of the existing manual file based system that was in use, it was compared to the proposed system. A prototype of a proposed system was developed to ease data access, security and retrieval for instant report production by the prison management. The prototype was developed using MySql database, PHP, CSS, JavaScript and HTML.