Department of Artificial Intelligence

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https://bdears.busitema.ac.ug/handle/123456789/699

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Browsing Department of Artificial Intelligence by Title

Now showing 1 - 7 of 7
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    A field-based recommender system for crop disease detection using machine learning
    (Frontiers in Artificial Intelligence, 2023) Omara, Jonathan; Talavera, Estefania; Otim, Daniel; Turcza, Dan; Ofumbi, Emmanuel; Owomugisha, Godliver
    This study investigates crop disease monitoring with real-time information feedback to smallholder farmers. Proper crop disease diagnosis tools and information about agricultural practices are key to growth and development in the agricultural sector. The research was piloted in a rural community of smallholder farmers having 100 farmers participating in a system that performs diagnosis on cassava diseases and provides advisory recommendation services with real-time information. Here, we present a field-based recommendation system that provides real-time feedback on crop disease diagnosis. Our recommender system is based on question–answer pairs, and it is built using machine learning and natural language processing techniques. We study and experiment with various algorithms that are considered state-of-the-art in the field. The best performance is achieved with the sentence BERT model (RetBERT), which obtains a BLEU score of 50.8%, which we think is limited by the limited amount of available data. The application tool integrates both online and online services since farmers come from remote areas where internet is limited. Success in this study will result in a large trial to validate its applicability for use in alleviating the food security problem in sub-Saharan Africa. KEYWORDS Crop disease monitoring, recommendation systems, natural language processing, smart farming, question-answer pairs, food security
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    A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol
    (Elsevier, 2023) Owomugisha, Godliver; Nakatumba-Nabende, Joyce; Dhikusooka, Joshua Jeremy; Taravera, Estefania; Nuwamanya, Ephraim; Mwebaze, Ernest
    In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screen house and open field, respectively, until disease symptoms were visibly seen by the human eye. Keywords: Spectral data protocol, Cassava diseases, Crop diagnosis, Smart agriculture, Early disease detection
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    A light spectrometer device for crop disease monitoring
    (ICLR, 2023) Dhikusooka, J Joshua; Nuwamanya, Ephraim; Talavera, Estefania; Owomugisha, Godliver
    Portable devices for the early detection of crop diseases are needed to support the farmers working in the field. Spectrometers showed their potential in the detection of crop diseases. However, high interpretation skills are needed to use the currently available spectrometers. In this project, we propose a portable device that obtains a spectrum wavelength of 700 nanometers describing the information of the crop. The output of this tool is integrated into a smartphone in the form of an app, making it accessible for use in the field in real applications.
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    A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field
    (Association for Computing Machinery, 2020) Owomugisha, Godliver; Mugagga, K. B. Pius; Melchert, Friedrich; Mwebaze, Ernest; Quinn, A. John; Biehl, Michael
    We present our initial proof of concept study towards the development of a low-cost 3-D printed smartphone add-on spectrometer. The study aimed at developing a cheap technology (less than 5USD) to be used for detection of crop diseases in the field using spectrometry. Previously, we experimented with the problem of disease diagnosis using an off-the-shelf and expensive spectrometer (approximately 1000 USD). However, in real world practice, this off-the-shelf device cannot be used by typical users (smallholder farmers). Therefore, the study presents a tool that is cheap and user friendly. We present preliminary results and identify requirements for a future version aiming at an accurate diagnostic technology to be used in the field before disease symptoms are visibly seen by the naked eye. Evaluation shows performance of the tool is better than random however below performance of an industry grade spectrometer. CCS CONCEPTS • Applied computing → Physical sciences and engineering; • Computing methodologies → Machine learning. KEYWORDS Low-cost, Spectrometery, Crop disease, Diagnosis, 3-D printed, Smartphone
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    Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
    (MDPI, 2022) Mafukidze, Dzingai Harry; Owomugisha, Godliver; Otim, Daniel; Nechibvute, Action; Nyamhere, Cloud; Mazunga, Felix
    Convolutional neural networks (CNNs) are the gold standard in the machine learning (ML) community. As a result, most of the recent studies have relied on CNNs, which have achieved higher accuracies compared with traditional machine learning approaches. From prior research, we learned that multi-class image classification models can solve leaf disease identification problems, and multi-label image classification models can solve leaf disease quantification problems (severity analysis). Historically, maize leaf disease severity analysis or quantification has always relied on domain knowledge—that is, experts evaluate the images and train the CNN models based on their knowledge. Here, we propose a unique system that achieves the same objective while excluding input from specialists. This avoids bias and does not rely on a “human in the loop model” for disease quantification. The advantages of the proposed system are many. Notably, the conventional system of maize leaf disease quantification is labor intensive, time-consuming and prone to errors since it lacks standardized diagnosis guidelines. In this work, we present an approach to quantify maize leaf disease based on adaptive thresholding. The experimental work of our study is in three parts. First, we train a wide variety of well-known deep learning models for maize leaf disease classification, then we compare the performance of the deep learning models and finally extract the class activation heatmaps from the prediction layers of the CNN models. Second, we develop an adaptive thresholding technique that automatically extracts the regions of interest from the class activation maps without any prior knowledge. Lastly, we use these regions of interest to estimate image leaf disease severity. Experimental results show that transfer learning approaches can classify maize leaf diseases with up to 99% accuracy. With a high quantification accuracy, our proposed adaptive thresholding method for CNN class activation maps can be a valuable contribution to quantifying maize leaf diseases without relying on domain knowledge. Keywords: CNN; transfer learning; class activation heatmap; adaptive thresholding
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    Early detection of plant diseases using spectral data
    (APPIS, 2020) Owomugisha, Godliver; Nuwamanya, Ephraim; Quinn, A. John; Biehl, Michael; Mwebaze, Ernest
    Early detection of crop disease is an essential step in food security. Usually, the detection becomes possible in a stage where disease symptoms are already visible on the aerial part of the plant. However, once the disease has manifested in different parts of the plant, little can be done to salvage the situation. Here, we suggest that the use of visible and near infrared spectral information facilitates disease detection in cassava crops before symptoms can be seen by the human eye. To test this hypothesis, we grow cassava plants in a screen house where they are inoculated with disease viruses. We monitor the plants over time collecting both spectra and plant tissue for wet chemistry analysis. Our results demonstrate that suitably trained classifiers are indeed able to detect cassava diseases. Specifically, we consider Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) applied to original spectra and, alternatively, in combination with dimension reduction by Principal Component Analysis (PCA). We show that successful detection is possible shortly after the infection can be confirmed by wet lab chemistry, several weeks before symptoms manifest on the plants.
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    Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases
    (IEEE, 2021) Owomugisha, Godliver; Melchert, Friedrich; Mwebaze, Ernest; Quinn, A. John; Biehl, Michael
    We discuss the use of matrix relevance learning, a popular extension to prototype learning algorithms, applied to a three-class classification task of diagnosing cassava diseases from spectral data. Previously this diagnosis has been done using plant image data taken with a smartphone. However for this method disease symptoms need to be visible. Unfortunately for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. This research is premised on the hypothesis that diseased crops without visible symptoms can be detected using spectral information, allowing for early interventions. In this paper, we analyze visible and near-infrared spectra captured from leaves infected with two common cassava diseases (cassava brown streak disease and cassava mosaic virus disease) found in Sub-Saharan Africa. We also take spectra from leaves of healthy plants. The spectral data come with thousands of dimensions, therefore different wavelengths are analyzed in order to identify the most relevant spectral bands for diagnosing these disease. To cope with the nominally high number of input dimensions of data, functional decomposition of the spectra is applied. The classification task is addressed using Generalized Matrix Relevance Learning Vector Quantization and compared with the standard classification techniques performed in the space of expansion coefficients. INDEX TERMS Cassava disease diagnosis, feature selection, matrix relevance learning, spectral data.