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Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%., Competing Interests: This work is funded in part by the National Center of Big data and Cloud Computer (NCBC), University of Engineering and Technology, Peshawar, under the auspices of Higher Education Commission, Pakistan and Pakistan Tobacco Board (PTB). Commercial funding: This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Medicinal flora plays a vital role in treating various types of ailments in living beings. The present study was planned to investigate and document systematically the indigenous knowledge in a scientifically little explored area of Ladha sub-division, South Waziristan agency, Pakistan. Hence, this study would contribute positively to the field of ethnopharmacology. Methods: Prior to ethnomedicinal data collection, regular field visits were conducted during the month of May and June 2015 to locate the sites and respondents from where the traditional knowledge was to be recorded. Ethno-medicinal data was collected during the month July and August 2015 through rapid appraisal approach (RAA) based on direct interaction with the indigenous communities by making group discussions, corner meetings and semi-structured interviews. Data was evaluated statistically by using the index of Use value (UV) and Frequency of citations (FC). Results: A total of 82 medicinal plants belonging to 42 families were reported in the study. Leaves were the most frequently used plant parts. Highest use values were recorded for Peganum harmala (0.93), Punica granatum (0.91), Thymus mongolicus (0.90), Chenopodium album (0.89), Coriandrum sativum (0.87), Mentha longifolia (0.87), Lactuca serriola (0.87) and Portulaca oleracea (0.87). Medicinal plants used for the gastro intestinal complexities and respiratory diseases were more than 9% followed by skin and diarrhea (7% each), liver disorders (5%) cough and cold fever (5%). Conclusion: People of the area mostly still rely on traditional herbal therapies. Keeping in mind the dependence of the indigenous community for their primary health care on such herbal remedies, pharmacological and critical toxicological investigation of certain flora is necessary. Moreover, projects should be designed to analyze the existing issues and problems related with medicinal plants conservation. [ABSTRACT FROM AUTHOR]

Abstract: The Northern Areas of Pakistan form a mountainous, semi-arid region in which subsistence mixed farming is the predominant economic activity for the majority of the population, which numbers around 1 million. Following a period of relative isolation, construction of the Karakoram Highway and the ensuing development activity have been catalysts for rapid infrastructural and social change over the last two decades. In the study reported here, feed resources for the livestock enterprise, which is largely made up of cattle, goats and sheep, were studied in detail over the course of a single winter feeding season. Productive outputs including liveweight change, milk production and reproductive performance were also quantified over a full year. The aim of the study was to provide a quantitative description of the system and to assess the extent to which proximity to a major highway influenced livestock husbandary. Six villages across the region were selected for study as part of a 2×3 factorial design with one village per cell of the study design. Factors consisted of two geographical transects and three agro-ecological zones. Transects were the Karakoram Highway (KKH) transect which enjoyed relatively good transport infrastructure and the Gilgit Ghizer Region (GGR) transect where infrastructure was more limited. Agro-ecological zones were the single, transitional and double cropping zones. One village per transect from each of the three main agro-ecological zones was chosen for study with 6–7 households within each village studied. Results showed that feed resources per household did not vary significantly according to transect or zone but cattle numbers per household were higher in the GGR transect than in the KKH transect indicating a heavier reliance on subsistence livestock production in this transect. Live weight and body condition of livestock in the Northern Areas changed markedly over the annual cycle. Losses of live weight were in the order of 10% over winter while summer gains during the summer season averaged 35% of initial live weight. There were differences between transects, with animals in the KKH gaining less weight in summer but showing higher milk yield (in cattle) and better reproductive performance. The results indicate a typical system of smallholder livestock production with heavy reliance on cereal by-products, a mix of livestock species and a relative scarcity of stored feed resources relative to overall livestock holdings. This study also suggests that proximity to the main highway running through the Northern Areas is associated with a reduced but more feed-efficient livestock production system. [Copyright &y& Elsevier]