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 management was mainly 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 in managing insect pests and weeds, 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.

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.

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.

Weeds with allelopathic effect have been reported to cause significant damage in agriculture particularly in smallholder farming systems. This study assessed the allelopathic effects of different concentrations of crude extract of a noxious weed Sphaeranthus suaveolens on seed germination and seedling growth of Phaseolus vulgaris and Oryza sativa by examining germination, seedling height, and total chlorophyll content after seven and fourteen days of crude extract treatment, respectively. Results showed that seed germination and seedling growth were significantly ( ) decreased with increase in concentration of crude extract, signifying concentration dependency. Highest concentration (100%) of S. suaveolens crude extracts resulted in 90% and 100% inhibition of P. vulgaris and O. sativa seed germination, respectively. Chlorophyll content, fresh weight, and root and shoot length of both P. vulgaris and O. sativa were also significantly ( ) affected by highest concentration (100%) of S. suaveolens crude extracts. Results from this study suggest that the extract of S. suaveolens contains water-soluble allelochemicals which significantly reduce growth and productivity of P. vulgaris and O. sativa.

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 Tanzania, using Samsung Galaxy A03 smartphones with 8-megapixel camera. Researchers, farmers and agricultural extension officers were trained to capture images under diverse conditions, including varying lighting, angles and backgrounds to ensure the dataset is diverse and representative. Plant pathologists were used to validate the images to ensure and enhance the reliability of the labels. Pre-processing steps such as duplicate removal, filtering of irrelevant images, annotation and metadata integration were applied resulting 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 accessibility for researchers working in the field of plant diseases. This dataset holds significant potential for reuse in training machine learning models for crop disease detection, transfer learning and data augmentation studies. By enabling early detection and classification of potato diseases, the dataset supports the development of innovative agricultural 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.

Irish potato ( Solanum tuberosum ) is an important food and cash crop for smallholder farmers in Tanzania, particularly in the Southern Highlands. Despite these, yields remain low due to persistent challenges from pests and diseases, threatening both productivity and household incomes. This study examined knowledge, perceptions and practices of smallholder farmers in managing pests and diseases of Irish potato in Mbeya, Tanzania. A total of 225 farmers from five wards were surveyed using structured interviews and focus group discussions. Most respondents (83%) identified pests and 73% identified diseases as major constraints. Aphids (83%), whiteflies (71%) and potato tuber moth (39%) were the most commonly reported pests, while early blight (91%), late blight (45%) and Fusarium wilt (29%) were the most cited diseases. Despite the widespread use of chemical pesticides (92%) and fungicides (72%), access to these inputs was constrained by high costs, limited availability and insufficient knowledge, with only 24% of farmers reporting effective use. Non-chemical methods, such as crop rotation, intercropping and botanical extracts, were rarely practiced, reflecting low awareness and limited extension support. Most farmers relied on experience rather than consulting agricultural officers, and over 90% used traditional seed varieties. Yields varied significantly among wards, with averages ranging from 1.1 to 22.4 t/ha. While farmers demonstrated awareness of pests and diseases, management practices were heavily dependent on synthetic chemicals, with minimal integration of sustainable strategies. These findings highlight the need to promote integrated pest and disease management approaches tailored to local conditions for improving productivity of Irish potato.