In low- and middle-income countries, inadequate sanitation results in faecal contamination of the water used by urban farmers for irrigation. Consumers of raw contaminated vegetables run the risk of developing diarrhoeal diseases and helminth infections, which are a leading cause of under-five mortality and impact the well-being and productivity of millions of adults. This review identifies the evidence base for assessing which factors determine the success and/or failure of interventions that aim to manage the risk of faecal contamination in the urban irrigated vegetable value chain. We carried out a systematic search of the literature from the perspective of the COM-B behaviour framework (Capability þ Opportunity þ Motivation ¼ Behaviour). Our results reveal that most interventions address stakeholders’ opportunity or capability to adopt safe practices without adequately considering their motivation. Interventions often focus on one sector rather than on the whole value chain (sanitation, agriculture, trade, consumption). To effectively change hygiene and food safety practices in the urban irrigated vegetable value chain, stakeholders’ intrinsic motivations need to be identified. Where WHO’s multi-barrier approach is the best option, we recommend building on local multistakeholder platforms and adopting a behaviour change framework to support the largely technical change from farm to fork.

Purpose and Methods: Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference.; Results: The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier.; Conclusion: The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics.