Support Vector Machine in Precision Agriculture: A review

Nano Sensors are sensing devices with a dimension of less than or equal to 100 nm. They are incredibly tiny devices that transform physical, chemical, or biological substances into detectable signals. Because of this device's capacity to detect physical and chemical changes, nanotechnology has emerged as a technology of choice in a variety of industries. The device provides efficient and cost-effective methods for detecting and measuring chemical and physical characteristics. This overview discusses the status of Nano Sensors, as well as their accomplishments and potential applications toward downstream targets in medical, security, agriculture as well in Covid-19 detection. The paper provides a summary and critical analysis of various architectures (structures) employed in the development and use of Nano Sensors. Surface engineering is used to generate diverse chemistries for both general and specialised purposes. We derived fresh findings from available data on the mechanism, prospective development of various structures, approaches, and applications, and highlighted the contrasts and similarities in their characteristics and working processes. The review further summarised ability and future expected of this sensor in dealing with the various challenges where different nano sensors, types, fabrication techniques and applications with highlighted novelties of these techniques and applications are presented. 

Nanosensors: Recent Perspectives on Attainments and Future Promise of Downstream Applications

Industrial and developmental activities have raised heavy metal loads in wastewater and their inappropriate treatment before disposal causes grave dilemma to the environment. Chromium (Cr), cadmium (Cd) and lead (Pb) are highly toxic heavy metals and extensively used hence, excessively generated with disposing effluent from the industries like paint, battery, electroplating, tannery, paper and pulp, textile, smelting and mining has raised the public attention owing to health concerns. Microbial-remediation processes are considered as environmentally benign options and their integration with existing treatment techniques can construct advanced types of systems in terms of their cost and efficiency due to their synergistic action. These microbial-integrated techniques are gaining popularity these days. There are varieties of microbial-integrated strategy which has been investigated carefully for efficient removal of Cr, Cd, and Pb and has been published in leading journals. In this review, we have critically discussed the microbial–integrated strategies and recent advances in microbial-integrated techniques for proficient removal of Cr, Pb, and Cd from industrial wastewater in a cheaper and cleaner way. In addition, the advantages and disadvantages of the integrated system also have been discussed in this review. The microbial integrated systems such as bioelectrochemical, photo-bioelectrochemical, nano-microbial, and constructed wetland showed excellent performance and have tremendous potential for large scale exploitation. To conclude, microbial-integrated techniques proved as excellent remedial options for Cr, Pb and Cd contaminated wastewater that has been discussed well in this review. These integrated processes may be cost-effective, viable, more efficient and less time taking than the existing techniques and have intense opportunity. 

A review on microbial-integrated techniques as promising cleaner option for removal of chromium, cadmium and lead from industrial wastewater

Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations. 

A Review on UAV-Based Applications for Plant Disease Detection and Monitoring

As a major global pest, fall armyworm (FAW), Spodoptera frugiperda, invaded China in 2019, which has seriously threatened the safety of China's food production and raised widespread concerns. As a new low-volume application technology, an unmanned aerial spray system (UASS) is playing an important role in the control of FAW in China. However, the studies on the effect of the water application volume on the efficacy of FAW using UASS have been limited. In this study, Kromekote® cards were used to sample the deposition. The method of using a sampling pole and sampling leaf for the determination of deposition. Four water application volumes (7.5, 15.0, 22.5, and 30.0 L/ha) were evaluated with regard to the corn FAW control efficacy. A blank control was used as a comparison. The control efficacy was assessed at 1, 3, 7, and 14 days after treatment (DAT). The tested results showed that sampling methods have a significant effect on deposition results. The number of spray deposits and coverage on the sampling pole were 35 and 40% higher than those on the sampling leaves, respectively. The deposition and control efficacy gradually increased as the water application volume increased. The control efficacy at 14 DAT under different water application volumes was in the range of 59.4–85.4%. These data suggest that UASS spraying can be used to achieve a satisfying control of FAW, but the control efficacy of the water application volume of 30.0 and 22.5 L/ha did not differ significantly. Considering work efficiency, a water application volume of 22.5 L/ha is recommended for field operation.

Control Efficacy and Deposition Characteristics of an Unmanned Aerial Spray System Low-Volume Application on Corn Fall Armyworm Spodoptera frugiperda

Unmanned Aerial Vehicle (UAV) spray has been used for efficient and adaptive pesticide applications with its low costs. However, droplet drift is the main problem for UAV spray and will induce pesticide waste and safety concerns. Droplet size and deposition distribution are both highly related to droplet drift and spray effect, which are determined by the nozzle. Therefore, it is necessary to propose an evaluating method for a specific UAV spray nozzles. In this paper, four machine learning methods (REGRESS, least squares support vector machines (LS-SVM), extreme learning machine, and radial basis function neural network (RBFNN)) were applied for quantitatively evaluating one type of UAV spray nozzle (TEEJET XR110015VS), and the case of twin nozzles was investigated. The results showed REGRESS and LS-SVM are good candidates for droplet size evaluation with the coefficient of determination in the calibration set above 0.9 and root means square errors of the prediction set around 2 µm. RBFNN achieved the best performance for the evaluation of deposition distribution and showed its potential for determining the droplet size of overlapping area. Overall, this study proved the accuracy and efficiency of using the machine learning method for UAV spray nozzle evaluation. Additionally, the study demonstrated the feasibility of using machine learning model to predict the droplet size in the overlapping area of twin nozzles.

Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle

In the demand for renewable energy sources, algae are considered to have a good potential for biodiesel production. Fast detection of heavy metals in energetic algae is of vital importance for algal biomass and biodiesel production environment monitoring. This study aims to determine cadmium (Cd) and zinc (Zn) contents in algal pellets with laser-induced breakdown spectroscopy (LIBS) technology. Partial least squares (PLS) regression and extreme learning machine (ELM) were constructed after spectral preprocessing by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Savitzky–Golay smoothing (SG). For univariate analysis, ELM models based on Cd II 226.45 nm and Zn II 206.19 nm of SNV preprocessed spectra achieved the best results for Cd and Zn content prediction respectively, with Rp, RMSEP and RPD values of 0.9866, 50.71 mg kg−1 and 6.95 for the former, and 0.9873, 30.08 mg kg−1 and 6.44 for the latter. Multivariate analysis of Cd based on the PLS model with global spectra achieved the best performance with Rc and Rp values of 0.9965 and 0.9972, RMSECV and RMSEP values of 25.57 mg kg−1 and 23.63 mg kg−1, and an RPD value as high as 13.27, showing the excellent robustness and effectiveness of the model for Cd detection. For Zn analysis, the best performance was achieved by the ELM model based on feature variables selected by regression coefficients, with Rc and Rp values of 0.9836 and 0.9920, RMSEC and RMSEP values of 34.25 mg kg−1 and 24.64 mg kg−1, and an RPD value of 7.87. The results indicated that the LIBS technique combined with appropriate preprocessing algorithms and multivariate chemometric methods could be a rapid and accurate way for quantitative analysis of Cd and Zn contents in algae and the aquatic environment.

Quantitative analysis of cadmium and zinc in algae using laser-induced breakdown spectroscopy

The invention discloses a crop disease monitoring method and system based on UAV multi-source image fusion and belongs to the technical field of agricultural information monitoring. The system comprises a UAV equipped with an image capture device and a processor used for processing images and detecting disease distribution. The UAV flies according to a determined capture route and captures the hyperspectral image, the RGB image and the thermal infrared image of farm crops. The processor comprises an image processing unit, a modeling unit and a wireless remote control unit. The captured hyperspectral image, RGB image and thermal infrared image are separately spliced, a hyperspectral spliced image and the RGB spliced image are merged, and a thermal infrared spliced image and the RGB splicedimage are merged. Thus, the advantages of respective images can be integrated, the complementarity of different types of remote sensing images can be achieved, image data sets conducive to human interpretation and post-processing are available, and a possibility can be provided for accurate crop disease monitoring.

Crop disease monitoring method and system based on UAV multi-source image fusion

The invention discloses an unmanned aerial vehicle remote-sensing pesticide-spraying integrated method and apparatus based on visible light thermal infrared image fusion, and belongs to the technicalfield of crop plant-protection pesticide-spraying. The method comprises the following steps: 1) setting a flight route; 2) respectively collecting visible light images and thermal infrared images of farmland crops by using a visible light camera and a thermal infrared camera, and transmitting the images to a processor; 3) successively performing registration and fusion on the visible light imagesand the thermal infrared images, and extracting outline and color feature information of disease spots from fusion images; 4) establishing an discrimination model of crop disease types based on the outline and color feature information of disease spots in the fusion images; 5) repeating steps 1) to 3), and performing crop disease type discrimination on the extracted fusion images according to thediscrimination model of crop disease types; and 6) controlling opening valves of different pesticide boxes according to the crop disease types. By fusion of the thermal infrared images and the visiblelight images, relatively more image information can be contained, and disease discrimination accuracy is improved.

Unmanned aerial vehicle remote-sensing pesticide-spraying integrated method and apparatus based on visible light thermal infrared image fusion

The utility model discloses a plant protection unmanned aerial vehicle weeding device of adaptation clinoform belongs to the crops plant protection and spouts medicine technical field, including setting up flight control system, the medical kit in the unmanned aerial vehicle main part and following the shower nozzle of drawing forth in the medical kit, the medical kit is equipped with along unmanned aerial vehicle main part transversal arrangement's spray lance, and the axial of following the spray lance is equipped with a plurality ofly and spray lance articulated spray tube, and the tip at the spray tube is installed to the shower nozzle, be equipped with the laser sensor who is used for acquireing the ultrasonic sensor of crop and weeds elevation information and is used for confirming weeds positional information on the spray tube. Through selecting the slope to spout the medicine route, carry out target discernment and location and confirming that high, angle and relative positionbetween shower nozzle and the weeds goes on accurate spouting the medicine to the target. Can adapt to the clinoform, the accuracy is carried out slope weeds location and is fixed a point to weed.

Plant protection unmanned aerial vehicle weeding device of adaptation clinoform

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