Search Results (28)

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management. Full article

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R² of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R² and RMSE were 0.927 and 0.12, respectively. The canopy coverage R² of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing. Full article

Promotion of research and development in advanced technology must be implemented in agriculture to increase production in the current challenging environment where the demand for manual farming is decreasing due to the unavailability of skilled labor, high cost, and shortage of labor. In the last two decades, the demand for fruit harvester technologies, i.e., mechanized harvesting, manned and unmanned aerial systems, and robotics, has increased. However, several industries are working on the development of industrial-scale production of advanced harvesting technologies at low cost, but to date, no commercial robotic arm has been developed for selective harvesting of valuable fruits and vegetables, especially within controlled strictures, i.e., greenhouse and hydroponic contexts. This research article focused on all the parameters that are responsible for the development of automated robotic arms. A broad review of the related research works from the past two decades (2000 to 2022) is discussed, including their limitations and performance. In this study, data are obtained from various sources depending on the topic and scope of the review. Some common sources of data for writing this review paper are peer-reviewed journals, book chapters, and conference proceedings from Google Scholar. The entire requirement for a fruit harvester contains a manipulator for mechanical movement, a vision system for localizing and recognizing fruit, and an end-effector for detachment purposes. Performance, in terms of harvesting time, harvesting accuracy, and detection efficiency of several developments, has been summarized in this work. It is observed that improvement in harvesting efficiency and custom design of end-effectors is the main area of interest for researchers. The harvesting efficiency of the system is increased by the implementation of optimal techniques in its vision system that can acquire low recognition error rates. Full article

Pakistan faces water scarcity and high operational costs for traditional irrigation systems, hindering agricultural productivity. Solar-powered irrigation systems (SPIS) can potentially provide a sustainable and affordable solution, but face technical, financial and policy barriers to adoption. A comprehensive study is needed to examine feasibility and identify barriers. Therefore, a comprehensive review study is conducted to identify the potential for solar irrigation, key issues and challenges related to its implementation in Pakistan. The analysis is based on published studies, technical reports and a survey of solar-powered drip irrigation systems. The use of SPIS in Pakistan is becoming a cost-effective and sustainable option for irrigation, particularly in remote and off-grid areas. However, these systems also have their challenges, such as high initial costs, maintenance and repairs, limited access to spare parts, lack of government policies and regulations, lack of technical expertise, lack of financing options and social acceptance. The most pressing issue is the risk of groundwater exploitation by using SPIS. Based on the analysis of the energy and water situation in Pakistan, it is important to sustainably use both solar energy and groundwater resources, through the implementation of effective management strategies and policies. With the right policies and investment in research and development of SPIS and groundwater, farmers can benefit by increasing crop yields, conserving water resources, reducing the cost of energy, increasing productivity and improving the standard of living and access to electricity in remote and off-grid areas. It is recommended that the adoption of solar energy be promoted to run high efficiency irrigation systems (HEIS) with urgent capacity improvement among farmers, advisors and system installers to sustainably manage water resources in SPIS. This would not only help to reduce the consumption of fossil fuels and associated environmental impacts, but also increase farmers’ income and reduce their operational costs. Moreover, the use of SPIS can improve crop yields, leading to food security and poverty reduction. Thus, the government and policymakers should consider implementing policies and incentives to encourage the large-scale adoption of solar energy in the agricultural sector. Full article

The variability in soil properties and crop yield can be overcome by adoption of smart farming practices through interpolation and mapping of spatial variability patterns. Geospatial technologies can be utilized to determine the cause of spatial variability in fields for site-specific application of fertilizer. This study was designed to quantify and identify the spatial variation in soil properties and wheat (Triticum aestivum L.) yield and to delineate prescription maps for precise application of fertilizer in a semi-arid subtropical region of Pakistan. To examine the variability in soil properties on the production of the considered crop, this study comprised two different fields and each field was divided into (20 × 20 m) grids. The samples of soil were collected at 15 cm and 30 cm soil depths before the fertilization to analyze the different soil characteristics i.e., nitrogen (N), electrical conductivity (EC), potassium (K), soil organic matter (SOM), phosphorus (P), and pH. The boundaries of selected fields and grid points were established with a real-time kinematics-global positioning system (RTK-GPS). The soil data were acquired with a soil proximal sensor at a depth of 7 cm after fertilization. The statistical analysis coefficient of variation (CV), geostatistical-analysis-nugget-to-sill ratio (N:S), and the interpolated maps (ArcGIS pro 2.3) were used to characterize the least to moderate variability of soil parameters and yield, demanding site-specific management of fertilizer application. Cluster analysis was conducted using Minitab 21, which classified soil and yield characteristics into five categories: “very good”, “very low”, “good”, “poor”, and “medium”, with an external heterogeneity and internal homogeneity both more than 60%. Significant relationships (p < 0.05) between soil and crop properties were used to develop the management zones (MZs) for the precise application of fertilizer in wheat fields. Significant differences (p < 0.05) in soil nutrients were found in the very high and very low productivity zones at both sampling times, which suggest delineating the MZs for precise application of fertilizer according to the need of crop and soil properties. The results revealed that the optimum number of MZs for the wheat fields was five and there was heterogeneity in the soil nutrients in five MZs. The findings of this study also highlight the necessity of predicting the crop and soil factors by using precision technologies to develop the prescription maps, because sampling and analysis of soil are expensive and time-consuming. Based on the demand of the soil and crops, site-specific fertilization can increase economic and environmental efficiency. Full article

Rainfall is a key factor in hydrological, meteorological, and water management applications in restricted regions or basins, but its measurement remains difficult in mountainous or otherwise remote places due to a lack of readily available rain gauges. While satellite rainfall data offer a better temporal resolution than other sources, the majority of this data are only available at a coarse geographic resolution, which distorts the true picture of precipitation. Thus, researchers at the University of Agriculture in Faisalabad used the normalized difference vegetation index (NDVI) monthly data and 1 km topography data for the whole Indus Basin from 2002 to 2011 to reduce the TRMM’s spatial resolution from 25 km to 1 km. An approach to downscaling based on a regression model with residual correction was established in this study. First, we resampled the NDVI and TRMM datasets to a 25 km resolution and established a regression model connecting the two datasets. Precipitation was forecasted at a distance of 25 km. The TRMM 3B43 product was then adjusted downward by the projected precipitation to achieve the residual value. The IDW method was used to reduce the resolution of the residual image from 25 km to 1 km. Rainfall was predicted using a regression model applied to NDVI at a 1 km spatial resolution. The final downscaled precipitation was created by combining the modeled precipitation at 1 km resolution with the residual image. The result was double-checked by the post-processing steps of validation and calibration. Full article

Precision agriculture is getting immense attention from researchers and farmers across the world due to the threatening situation of the demand and production gap. Evolution in the electromechanical system and the emergence of intelligent monitoring and conditioning systems have enabled closing the gap to make agronomy quicker, lesser prone to infestations, and still profitable at the same time. Whereas the Internet of Things (IoT) has enabled access to relevant data remotely and automates essential response systems to any threat or requirement by a plant in a particular environment. This study concentrates on gathering such advanced mechatronic techniques in the agricultural sector and analyses of the benefits and disadvantages of the modern method. Full article

Soil moisture sensors and hydraulic modeling play a vital role in managing surface irrigation systems. Crop water productivity can be improved by managing the inflow cut-off time and optimizing the other field scale measurements. As such, hydraulic modelling and field experiments were carried out at the University of Agriculture Faisalabad-Pakistan. The soil moisture sensor (SEN-13322) and the WinSRFR model were used for this purpose. In total, nineteen treatments including eighteen simulated treatments and one conventional treatment were designed at two levels of discharge (Q₁:0.0025 and Q₂:0.0035 m³s⁻¹), at three sensor positions (S₁:55%, S₂:65%, and S₃:75%) across the field length, as well as with three different border widths (B₁:6.4m, B₂:8.5m, and B₃:10.7m) after successful sensor and model calibration during the two growing seasons of 2016–2017 and 2017–2018. The results revealed a significant difference between the means and the treatment T₁₀ i.e., Q₂S₁B₁ that were found to be highly efficient and uniform. Full article

Agriculture is extremely vulnerable to climate change, creating more difficult challenges. Presently, the agricultural sector contributes to between 19 and 29% of all global greenhouse Gas (GHG) emissions. Methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂) are the main types of greenhouse gases generated by the agricultural industry. Energy use before and after farms, as well as shifting ground carbon stocks above and below as a result of changes in land use, are major sources of CO₂ emissions. There has been a trend in recent years toward lowering GHG emissions in the agriculture sector. Precision agriculture Technologies (PAT) address the field’s temporal and spatial variability to maximize the usage of agricultural inputs (i.e., irrigation, fuel, and fertilizers). The PAT can keep or increase productivity while lowering GHG emissions from agricultural activities, whereas the variable rate irrigation (VRI) approach is helpful in this scenario. Recent research shows that VRI has a significant potential to mitigate GHG. The present study reviews research related to VRI that address the reduction in GHG emissions. Full article

Due to the intense seasonality, high labor intensity, and high cost, picking fruit and vegetables typically requires a significant amount of personnel, material resources, and time. The fruit and vegetable picking is a critical role in the agricultural production chain. At the same time, the world is facing the challenge of an aging population. As a result, the requirements of current agricultural output cannot be addressed by using the traditional ways of picking. The robots picking have been widely utilized in the domains of fruit and vegetable production due to increases in labor productivity, picking efficiency, cost, and other aspects related to the industry. Therefore, the structural characteristics and target recognition methods for the end-effectors of picking robots will be thoroughly summarized. This study will ensure that the future direction of structural development and recognition methods that are matched with fruit and vegetable picking are more visible. Full article

Promising Land and Water Conservation Practices (LWCPs) play a vital role in restraining floods and keeping the land productive. Floods are usually destructive and increase the risk of drowning, waterborne diseases, malnutrition, and multiple long-term knock-on effects. Similarly, the 2022 monsoon season has prompted the most severe flood in Pakistan. The main objective of this review is to highlight the importance of LWCPs as an adaptation strategy for flood mitigation. Moreover, different LWCPs are discussed concerning studies carried out in different regions and published in scientific journals, technical reports, and notes from experts. It was observed that both the in situ and ex situ LWCPs have a significant effect on reducing land degradation and flood control. Additionally, most of the reviewed studies showed a positive impact of LWCPs on agricultural productivity, primarily due to the retention of nutrients and moisture. Hence, land conservation practices including biological and agronomic measures (i.e., contour farming, conservation tillage, strip cropping, vegetation, etc.), and mechanical or engineering methods (i.e., check dams, bunding, ponds/reservoirs, etc.) are aimed at reducing the run-off velocity and mitigating the floods. The results of this study will encourage the stakeholders to adopt LWCPs to lessen flood hazards and uplift agricultural production by limiting the land degradation processes. Full article

The agriculture sector in Pakistan has a significant amount of potential in terms of solar, wind, geothermal, and biomass energy production and consumption. Most of the farm machinery on agriculture farms runs on high-cost fossil fuels and is also our source of greenhouse gas emissions (GHG). Utilizing renewable energy (RE) technology in the agriculture sector will reduce the cost of agricultural farming and will lower GHG emissions. The objective of this study is to determine and compare the solar and wind energy potential for the different districts in the Potohar region of Pakistan. The solar and wind energy data are obtained from HOMER Pro, RETScreen, and the weather station of the National Center of Industrial Biotechnology (NCIB), PMAS-Arid Agriculture University Rawalpindi. The results obtained are analyzed and compared which depicts that the Potohar region of Punjab, Pakistan has a significant amount of solar potential for its application in agriculture. This article gives an overview of renewable solar and wind energy potential and its applications for farmers and ranchers to make RE a rising source of energy and rural income in Pakistan. Full article

This research presents the new techniques and practical experiences of using unmanned aerial vehicles (UAVs) precision agriculture mapping. UAV-based remote sensing systems should be cost-effective, fast-producing, have high geometric accuracy, and be simple to operate by local staff. This work aims to: (1) precisely use high-resolution UAV thermal multi-spectral sensors and machine learning approaches to reliably assess crop water status on a field scale; (2) capture on-field images for quantitative study from the multi-spectral sensors; (3) establish workflows for digital agriculture applications; (4) interpret the intelligent irrigation decision model using UAV indices, maps, and multi-source heterogeneous data integration. This research gives us new methods to set an intelligent method for precision agriculture, which greatly improves the level of agricultural intelligence. Full article

Water is imperative for life and plays a vital role in sustaining multiple environmental services. Currently, water resources are under stress, and rainwater harvesting (RWH) is one of the solutions available to address water shortages. In this study, the potential of RWH for irrigation and recreational activities in a public park (i.e., Fatima Jinnah Park-Islamabad) is discussed. The soil conservation service-curve number (SCS-CN) method was used to estimate the runoff. Results revealed that annually 1.80 million cubic meters (MCM) runoff is generated at this park. If this runoff volume is accommodated and managed effectively, it can fulfill the requirements of irrigation and other water-related activities. Hence, the adoption of RWH technology is vital for managing water; therefore, this approach should be used to support any policy changes that lead to widespread use of RWH. Full article