Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop’s yield by efficiently removing weeds. Therefore, it helps decrease the environmental risks associated with traditional weed management approaches. However, to work efficiently and accurately, the weeding robot must have a robust weed detection mechanism to avoid physical damage to the targeted crops. This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots. The weed images were collected from six different agriculture farms in Pakistan. The dataset consisted of 9000 images of three crops: okra, bitter gourd, sponge gourd, and four weed species (horseweed, herb paris, grasses, and small weeds). We chose a single-shot object detection model, YOLO5. The selected model achieved a mAP of 0.88@IOU 0.5, indicating that the model predicted a large number of true positive (TP) with much less prediction of false positive (FP) and false negative (FN). While SSD-ResNet50 achieved a mAP of 0.53@IOU 0.5, the model predicted fewer TP with significant outcomes as FP or FN. The superior performance of the YOLOv5 model made it suitable for detecting and classifying weeds and crops within fields. Furthermore, the model was ported to an Nvidia Xavier AGX standalone device to make it a high-performance and low-power computation detection system. The model achieved an FPS rate of 27. Therefore, it is highly compatible with the laser weeding robot, which takes approximately 22.04 h at a velocity of 0.25 feet per second to remove weeds from a one-acre plot.

The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types.

The management of the abundant amount of used plastic bottle waste is a major concern nowadays, because it is a major contributor to landfills and overburdens waste processing facilities. Once disposed of, plastic can take centuries to break down, hence, recycling not only manages the waste efficiently, but it reduces the environmental impact and creates economic opportunities, as well. An incentive-based Reverse Vending machine (RVM) is an effective way to involve the general public in the management of plastic waste. The existing solutions are either very expensive, from a computation and cost perspective, or they lack the robustness and durability necessary for deployment. In this research, we have developed an incentive-based low-cost RVM for the collection, identification, classification, and sorting of used plastic bottles with the addition of a reward-based user application. The developed RVM includes a low-cost computing device running a lightweight image processing algorithm, sensors, camera, and a self-designed mechanical arm. To support the low computing device in the RVM, a lightweight MobileNet model has been trained using transfer learning. A dataset of 10,983 pet bottle images was collected using a camera installed inside the machine for the classification model. The results of the study show that MobileNet achieved 99.2% testing accuracy, which is better than the existing bottle-classification approaches. Moreover, the weight of the developed model was only 12 Mb, which is fourteen and six times less than inceptionV3 and Res-Net (Residual Neural Networks), respectively. Furthermore, the developed RVM costs a fraction of the price, compared to the existing solutions. Two versions of the machine have been deployed at a University for more than 6 months, collecting over 650 kg of plastic waste.

Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence increasing the need for automation in agriculture. The use of agricultural robots will help in the assurance of higher yields and proactive control of the crops. This study proposes a laser-based weeding vehicle with a unique mechanical body that is adjustable relative to the field structure, called the Robot Operating System (ROS) based robust control system, and is customizable, cost-effective and easily replicable. Hence, an autonomous-mobile-agricultural robot with a 20 watt laser has been developed for the precise removal of weed plants. The assembled robot’s testing was conducted in the agro living lab. The field trials have demonstrated that the robot takes approximately 23.7 h at the linear velocity of 0.07 m/s for the weeding of one acre plot. It includes 5 s of laser to kill one weed plant. Comparatively, the primitive weeding technique is highly labor intensive and takes several days to complete an acre plot area. The data presented herein reflects that implementing this technology could become an excellent approach to removing unwanted plants from agricultural fields. This solution is relatively cost-efficient and provides an alternative to expensive human labor initiatives to deal with the increased labor wages.

Growing population, uncontrolled migration, and unplanned urbanization are prevalent worldwide and Pakistan is no exception with Karachi ranking as Asia's fifth and the world's eleventh most populous metropolis. Urbanization is occurring at the expense of environmental degradation and a decline in the amount of Urban Green Spaces (UGS). The WHO recommends at least 9 square meters (m2) of green space per person, with a UGS value of 50 m2 per capital as the optimum green space for environmental sustainability. The study region consisted of six districts in Karachi, and their percentage of green space was calculated. Satellite images from the Landsat-8 Surface reflectance Tier-1 satellite program were used in this study. To perform calculations and detect land cover changes, the Normalized Difference Vegetation Index (NDVI), a widely used index to calculate the density of green area for a specific part of the land, is used. The results concluded that Karachi's green land cover has changed from 6.30% in January 2014 to 11.28% in January 2021, at NDVI > 0.3. These statistics of NDVI greater than 0.3 indicate sparse or moderate vegetation hence the possibility of including wild grass and bushes in the analysis is eliminated. Only a five percent (5%) rise over an eight-year period suggests the necessity for drastic action to improve the quantity and condition of UGS in Karachi. This study shows how Karachi's green spaces have been steadily being destroyed by uncontrolled urbanization and the yearly changes in vegetation. This research will help urban planners and policymakers to make wise choices for environmental sustainability in urban areas.