Browsing by Author "Laizer, Hudson"

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Allelopathic effects and bio-insecticidal potential of sphaeranthus suaveolens in common bean farming in Northern Tanzania
(NM-AIST, 2021-07) Laizer, Hudson
Weeds and insect pests are amongst the serious constraints in common bean farming in most parts of Tanzania. However, weeds with allelopathic and insecticidal properties if well manipulated can be used to manage insect pests, weeds and other challenges in agriculture. Therefore, this study assessed the allelopathic effects of a weed Sphaeranthus suaveolens in common bean seed germination and seedling growth, as well as its bio-insecticidal potential in managing Acanthoscelides obtectus, an insect pest of stored legumes. Social survey to smallholder farmers in Arumeru and Moshi rural districts, seed germination and post-harvest loss experiments, as well as the phytochemical screening of secondary metabolites were conducted in order to gather local knowledge on weed and insect pest management practices, assess the allelopathic effects of different concentration of S. suaveolens crude extracts, evaluate the bio-insecticidal properties and identify the secondary metabolites present in S. suaveolens crude extract. Results showed that, insect pests and weeds were the main constraints in common bean farming and chemical spray and mechanical weeding were the main methods used by farmers to manage them respectively. Furthermore, S. suaveolens in the surveyed villages was more distributed in farmlands and swampy areas and density was high during the rainy season. Results further showed that seed germination, seedling growth and chlorophyll content of common bean seedlings were significantly affected by the high concentration of S. suaveolens crude extracts which suggests the presence of water soluble allelochemicals. Moreover, the mortality of A. obtectus in common bean seeds treated with high dose of S. suaveolens powder was higher compared with the control experiment, signifying the insecticidal properties of S. suaveolens powder and its potential in managing A. obtectus in storage facilities. Additionally, the phytochemical analysis of S. suaveolens crude revealed the presence of terpenes, alkaloids, flavonoids, saponins, glycosides, steroids and anthraquinones. Thus, the findings from this study showed the allelopathic effects of S. suaveolens extracts on common bean seed germination and growth, as well as potential of its powder in managing A. obtectus in storage facilities thereby reducing post-harvest loss particularly among smallholder farmers with limited access to synthetic pesticides.
Common beans imagery dataset for early detection of bean rust and bean anthracnose diseases
(Elsevier, 2024-05-11) Laizer, Hudson; Mduma, Neema; Machuve, Dina; Maganga, Reinfrid
Common bean plays a crucial role in the agricultural sector in Tanzania. To most smallholder farmers, the crop serves as a principal source of protein and an essential source of income. Despite its significance, common bean production is often affected by diseases, particularly bean rust and bean anthracnose, resulting in low yields and diminished economic returns. To address this challenge, a comprehensive dataset of common bean leaf images has been collected by using smartphone cameras to capture the visual characteristics of healthy and diseased leaves. The dataset contains more than 59,072 labeled images, offering a valuable resource for developing machine learning models and user-friendly tools capable of early detection and diagnosis of bean rust and bean anthracnose diseases. The aim of generating this dataset is to facilitate the development of machine learning tools that will empower agricultural extension officers, smallholder farmers, and other stakeholders in agriculture to promptly identify and diagnose affected crops, enabling timely and effective interventions before causing significant economic loss. By equipping farmers with the knowledge and tools to combat these diseases, we can safeguard bean production, enhance food security, and strengthen the economic well-being of smallholder farmers in Tanzania and other parts of Africa.
Demography of lions (panthera leo) in Tarangire national park, Tanzania
(International Journal of Conservation Science, 2014) Laizer, Hudson; Tarimo, Thadeo M.C.; Kisui, Bernard
Tarangire lions reveal similar population characteristics to most of other lion populations in other protected areas in Africa. Tarangire lion population was estimated to be around 155 individuals as in June 2013 based on individual identification facilitated by the use of Radio telemetry coupled with the use of GPS to get information on individual lions within a specific pride to determine their location and characteristics. The population had a density of 7.5 lions per 100 square kilometers. The sex ratio was 1 male to 1.2 females more in favor of females but cubs had a sex ratio of 1 male to 1 female. The age composition was dominated by prereproductive age class (cubs and sub-adults), which constitutes 63.9% of the whole population. There was a total of 9 prides residing the park, the number of lions in the pride ranges from 238 individuals with the mean of 17.2 individuals. Cub survival was high with the average of 70.8% of all cubs born survived to year one. The overall population trend shows declining curve which suggests more conservation efforts are needed to make it stable.
Farmers’ Knowledge, Perceptions and Practices in Managing Weeds and Insect Pests of Common Bean in Northern Tanzania
(MDPI, 2019-07-28) Laizer, Hudson; Chacha, Musa; Ndakidemi, Patrick
Weeds and insect pests are among the serious constraints in common bean production in most rural communities. A survey of 169 smallholder farmers was conducted in two common bean-growing districts in northern Tanzania. The aim was to assess farmers’ knowledge, perceptions, current management practices and challenges in order to develop sustainable weed and insect pest management strategies. The results revealed that 83% of farmers perceived insect pests as the major constraint in common bean production, while 73% reported weeds as the main drawback. Insect pest managementwasmainly achieved through the use of synthetic pesticides, however, only 24%of farmers were able to apply, the rest could not afford due to high cost, limited access and lack of knowledge. Only 6.5% of farmers were aware of non-chemical methods and 2.1% did not practice any method in managing insect pests, both in the field and during storage. Moreover, farmers generally relied on experience inmanaging insect pests andweeds, and about 43%did not see the need to consult extension officers. These findings indicate that there is a need to sensitize and train farmers on the sustainable methods for pest and weed management in common bean farming systems in northern Tanzania.
Irish Potato Imagery Dataset for Detection of Early and Late Blight Diseases
(Elsevier, 2025-04-03) Neema Mduma; Laizer, Hudson
This dataset comprises of 58,709 annotated images of irish potato leaves, categorized into three classes (healthy, early blight and late blight). The data was collected over six months from smallholder farms in Southern Highlands Tan- zania, using Samsung Galaxy A03 smartphones with 8- megapixel camera. Researchers, farmers and agricultural ex- tension officers were trained to capture images under di- verse conditions, including varying lighting, angles and back- grounds to ensure the dataset is diverse and representative. Plant pathologists were used to validate the images to en- sure and enhance the reliability of the labels. Pre-processing steps such as duplicate removal, filtering of irrelevant im- ages, annotation and metadata integration were applied re- sulting in a high-quality dataset. The dataset is organized into three folders (healthy, early blight and late blight) and is freely available on the Zenodo repository to promote ac- cessibility for researchers working in the field of plant dis- eases. This dataset holds significant potential for reuse in training machine learning models for crop disease detec- tion, transfer learning and data augmentation studies. By en- abling early detection and classification of potato diseases, the dataset supports the development of innovative agricul- tural tools aimed at reducing crop losses and enhancing food security in Sub-Saharan Africa. Its robust design and regional specificity make it a valuable resource for advancing research and innovation in sustainable farming practices.
Machine Learning Imagery Dataset for Maize Crop: A Case of Tanzania
(Elsevier, 2023-03-31) Mduma, Neema; Laizer, Hudson
Maize is one of the most important staple food and cash crops that are largely produced by majority of smallholder farmers throughout the humid and sub-humid tropic of Africa. Despite its significance in the household food security and income, diseases, especially Maize Lethal Necrosis and Maize Streak, have been significantly affecting production of this crop. This paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania. The dataset is the largest publicly accessible dataset for maize leaves with a total of 18,148 images, which can be used to develop machine learning models for the early detection of diseases affecting maize. Moreover, the dataset can be used to support computer vision applications such as image segmentation, object detection and classification. The goal of generating this dataset is to assist the development of comprehensive tools that will help farmers in the diagnosis of diseases and the enhancement of maize yields thus eradicating the problem of fod security in Tanzania and other parts in Africa.
Prevalence of human schistosomiasis in various regions of Tanzania Mainland and Zanzibar: A systematic review and meta-analysis of studies conducted for the past ten years (2013–2023)
(Public Library of Science, 2024-09-09) Mbugi, Nicolaus; Laizer, Hudson; Chacha, Musa; Mbega, Ernest
Schistosomiasis is a significant public health problem in Tanzania, particularly for the people living in the marginalized settings. We have conducted a systematic review with meta-analysis on the prevalence of schistosomiasis to add knowledge towards the development of effective approaches to control the disease in Tanzania. Online databases namely, Pub Med, SCOPUS and AJOL, were systematically searched and a random effect model was used to calculate the pooled prevalence of the disease. Heterogeneity and the between studies variances were determined using Cochran (Q) and Higgins (I2) tests, respectively. A total of 55 articles met the inclusion criterion for this review and all have satisfactory quality scores. The pooled prevalence of the disease in Tanzania was 26.40%. Tanzania mainland had the highest schistosomiasis prevalence (28.89%) than Zanzibar (8.95%). Sub-group analyses based on the year of publication revealed the going up of the pooled prevalence, whereby for (2013–2018) and (2018–2023) the prevalence was 23.41% and 30.06%, respectively. The prevalence of the Schistosoma mansoni and Schistosoma hematobium were 37.91% and 8.86% respectively. Mara, Simuyu, and Mwanza were the most prevalent regions, with a pooled prevalence of 77.39%, 72.26%, and 51.19%, respectively. The pooled prevalence based on the diagnostic method was 64.11% for PCR and 56.46% for POC-CCA, which is relatively high compared to other tests. Cochrans and Higgins (I2) test has shown significant heterogeneity (p-value = 0.001 and I2 = 99.6). Factors including age, region, diagnostic method and sample size have shown significant contribution to the displayed heterogeneity. The pronounced and increasing prevalence of the disease suggests potential low coverage and possibly lack of involvement of some regions in the control of the disease. This, therefore, calls for an intensive implementation of control interventions in all endemic regions, preferably using an integrated approach that targets several stages of the disease lifecycle.