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The recent advancements in precision agriculture have led to a high demand for a portable and intelligent device capable of accurately measuring soil nutrients with reduced use of chemicals. Instead of relying on chemical-based laboratory tests, a smart portable device has been developed. The device measures soil nutrients, such as ammonium, calcium, potassium, nitrate, and chloride, and provides fertilizer dose recommendations. The device uses ion-selective electrodes, a microcontroller, multiplexer, operational amplifier, data logger, and input/output units. It reports soil health status to farmers or users via WhatsApp and e-mail. The measured soil nutrient values are uploaded to a database, which generates soil health cards and fertilizer dose recommendations. The device's performance was compared to standard methods, with a correlation coefficient of 0.9906 for ammonium, 0.9949 for calcium, 0.9852 for potassium, 0.9716 for nitrate, and 0.9716 for calcium. The sensor-based soil nutrient analyzer demonstrated similar performance to other established standard methods when tested on 250 soil samples of agricultural fields. Consequently, this device holds great potential for utilization in soil testing laboratories as well as for on-site soil monitoring in agricultural fields, specifically for the purpose of precision farming. [ABSTRACT FROM AUTHOR]

The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds. [ABSTRACT FROM AUTHOR]

Opencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth‐moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real‐time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real‐time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real‐time location of the vehicles on a 3D geo‐tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image‐processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine. [ABSTRACT FROM AUTHOR]