A review of Best Management Practices for potato crop using Precision Agricultural Technologies

Ahmad, Uzair; Sharma, Lakesh K.

doi:10.1016/j.atech.2023.100220

Cited by 20 publications

(17 citation statements)

References 119 publications

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“…The precision farming system relies on crop monitoring technology. Multispectral remote sensing satellites evaluate crop health, growth, and possible difficulties, enabling farmers to make informed decisions about irrigation, insect management, and nutrient delivery [185]. Crop rotation can enhance soil health; however, monitoring it necessitates spatiotemporal evaluation of crop-specific management.…”

Section: Precision Agriculturementioning

confidence: 99%

Pansharpening and spatiotemporal image fusion method for remote sensing

Anand,

Sharma

2024

Eng. Res. Express

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In last decades, remote sensing technology has rapidly progressed, leading to the development of numerous earth satellites such as Landsat 7, QuickBird, SPOT, Sentinel-2, and IKONOS. These satellites provide multispectral images with a lower spatial resolution and panchromatic images with a higher spatial resolution. However, satellite sensors are unable to capture images with high spatial and spectral resolutions simultaneously due to storage and bandwidth constraints, among other things. Image fusion in remote sensing has emerged as a powerful tool for improving image quality and integrating important features from multiple source images into one, all while maintaining the integrity of critical features. It is especially useful for high-resolution remote sensing applications that need to integrate features from multiple sources and hence a vital pre-processing step for various applications, including medical, computer vision, and satellite imaging. This review initially gives a basic framework for image fusion, followed by statistical analysis and a comprehensive review of various state-of-the-art image fusion methods, where they are classified based on the number of sensors used, processing levels, and type of information being fused. Subsequently, a thorough analysis of STF and pansharpening techniques for remote sensing applications has been covered, where the dataset of the DEIMOS-2 satellite is employed for evaluating various pansharpening methods while MODIS and Landsat images are employed in the spatiotemporal fusion method. A comparative evaluation of several approaches has been carried out to assess the merits and drawbacks of the current approaches. Several real-time applications of remote sensing image fusion have been explored, and current and future directions in fusion research for remote sensing have been discussed, along with the obstacles they present.

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Section: Precision Agriculturementioning

confidence: 99%

Pansharpening and spatiotemporal image fusion method for remote sensing

Anand,

Sharma

2024

Eng. Res. Express

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“…SOM mapping with meter-level spatial resolution can help to show the correlation between SOM and other elements of arable land, such as topographic features, soil moisture content, crop growth status, and farm yield, which can help us to analyze the relationship between various elements of cropland ecosystem comprehensively [43]. Compared with the results of medium spatial resolution mapping, the results of meter-level spatial resolution SOM mapping can accurately describe the details of highly localized soil-cultivated land, such as depicting the boundaries of degraded cultivated land, which can provide more accurate spatial information for precise management of farmland, reduce the uncertainty of precise management of farmland, and optimize the crop management methods such as fertilizer application and irrigation, to improve the efficiency of agricultural production [44]. For landscapes with strong heterogeneity, finer-resolution images have higher accuracy [45].…”

Section: Importance and Application Of Som Mapping At The Meter-levelmentioning

confidence: 99%

High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Ma,

Luo,

Meng

et al. 2024

Sustainability

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Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R² is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.

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“…The solution is to use advanced technologies for observing, measuring, and responding to different problems of agricultural management, called precision agriculture. Precision agriculture technologies have completely changed agricultural practices to grow crops, especially potatoes [1][2][3]. The best management practices include soil preparations, right irrigation, appropriate fertilizers along with its quantity, management strategies of pests and diseases, alongwith harvesting and storing tubers using precision postharvest tools [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Precision agriculture technologies have completely changed agricultural practices to grow crops, especially potatoes [1][2][3]. The best management practices include soil preparations, right irrigation, appropriate fertilizers along with its quantity, management strategies of pests and diseases, alongwith harvesting and storing tubers using precision postharvest tools [2,3]. A proper pest and disease management of potato at an early stage significantly impacts yield and quality of produce [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…The best management practices include soil preparations, right irrigation, appropriate fertilizers along with its quantity, management strategies of pests and diseases, alongwith harvesting and storing tubers using precision postharvest tools [2,3]. A proper pest and disease management of potato at an early stage significantly impacts yield and quality of produce [3,4]. From the literature, it is observed that 30 − 40% loss in potato production is due to disease and pests [5], which are caused by fungi, bacteria and viruses adversely affecting the productivity and quality of the produce.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Feature Fusion Ensemble of VGG16 and ResNet-50 for Automated Potato Leaf Abnormality Detection in Precision Agriculture

Trivedi,

Mahajan,

Maheshwari

et al. 2023

Preprint

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In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focused on disease due to different types of microbial infection in potato leaves using computer vision and machine learning approaches. In this paper, a data science approach for multiclass classification of potato normal and abnormal leaves due to fungal infection like early blight and late blight is performed using the ensembling of deep learning (DL) CNN models. Firstly, the performance of classification on potato disease is verified separately on VGG16 and ResNet−50 CNN models after pre-processing of the leaf dataset. The pre-processing includes noise removal and normalization. Further improvement in classification accuracy is achieved by the ensembling of VGG16 and ResNet−50 CNN models. The ensembling of CNN models is performed on the feature level by fusing features extracted using VGG16 and ResNet−50. From the experimental results, performed on publicly available datasets consisting of 2152 number of normal and abnormal images it is observed that the average classification accuracy of 98.22%, 96.16% and 95.68% is achieved using the proposed ensemble, VGG16 and ResNet−50 models respectively. The efficacy of the proposed approach (ensemble technique at feature level fusion) is verified in comparison with recently reported DL model-based approaches.

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A review of Best Management Practices for potato crop using Precision Agricultural Technologies

Cited by 20 publications

References 119 publications

Pansharpening and spatiotemporal image fusion method for remote sensing

Pansharpening and spatiotemporal image fusion method for remote sensing

High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Leveraging Feature Fusion Ensemble of VGG16 and ResNet-50 for Automated Potato Leaf Abnormality Detection in Precision Agriculture

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