Computational intelligence & modeling of crop disease data in Africa

The thesis presents the application of machine learning techniques to solve a real world challenge related to pest and disease control in the agricultural sector. The research is divided into three areas:i). We developed algorithms to auto-diagnose diseases in crops using an image dataset captured with a mobile phone camera. The study looked into disease incidence and severity measurements from cassava leaf images. We applied computer vision techniques to extract visual features of color and shape combined with classification techniques.(ii). We investigated on the diagnosis of disease in crops before they become symptomatic by use of spectrograms. The experiments of this study involved growing cassava plants in a screen house where they were inoculated with disease viruses and we monitored the plants over time collecting both spectral and plant tissue for wet chemistry analysis at each time step until the plants show disease. Our models in our case GMLVQ were able to detect cassava diseases one week after virus infection can be confirmed by wet lab chemistry, but several weeks before symptoms manifest on the plants.(iii). We investigated on the development of a low-cost 3-D printed smartphone add-on spectrometer that can be used to diagnose crop diseases in the fields. Moving from a commercial spectrometer (1000 USD), the study presented a tool that should be cheap (less than 5 USD ) and usable by smallholder farmers, thus improving their livelihoods through increased crop yields and food security

A field-based recommender system for crop disease detection using machine learning

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 offline 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.</p

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

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

A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol

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 screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.</p

A Low-Cost 3-D Printed Smartphone Add-on Spectrometer for Diagnosis of Crop Diseases in Field

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 5 USD) 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 can not 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

Comparative Analysis and Development of Mobile Device Authentication Framework for Corporate Networks

Several systematic reviews on mobile device technologies have been undertaken mostly identifying mobile security threats and challenges to corporate organisations' sensitive private information. This paper surveyed the existing level of secure authentication achieved by various mobile device-related frameworks against their listed goals. The solutions and security level of the existing authentication approaches among these categories were compared and improved on the KANYI BYOND framework by introducing a Radius server with the 802.11 authentications supported feature that provides access control to wireless routers, access points, hotspots in EAP/WPA-Enterprise/WPA2-Enterprise modes as means to achieve multiple authentications to mobile device users in corporate networks. Testing and validation of the resulting framework were done with the help of a riverbed modeler and a Denial of Service attack was simulated on all mobile devices' nodes in the designed network. The results indicated that the resulting framework provides multiple authentications and is thought to overcome self-reassuring by mobile device users on the network