Agricultural Monitoring, an Automatic Procedure for Crop Mapping and Yield Estimation: The Great Rift Valley of Kenya Case

Luciani, Roberto; Laneve, Giovanni; Jahjah, Munzer

doi:10.1109/jstars.2019.2921437

Cited by 29 publications

(6 citation statements)

References 60 publications

(61 reference statements)

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“…The fields with single crop types were easily distinguishable through their distinct spectral signatures, whilst crop types in intercropped fields (heterogeneous and fragmented) had many misclassifications. This was also the same with the Luciani et al [114] study, where the single crop fields of sorghum were often mistaken with maize intercropped fields, resulting in low classification accuracy.…”

Section: Mapping Intercropping Patternssupporting

confidence: 82%

“…The utilization of high spatial resolution sensors permits mapping such complex cropping patterns. Sentinel-2 and Landsat data have been explored for mapping intercropping of maize and sorghum and peas and wheat by Luciani et al [114] and Gumma et al [115], respectively. The results of their study highlighted the importance of the short-wave infrared region (SWIR) for crop identification.…”

Section: Mapping Intercropping Patternsmentioning

confidence: 99%

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Cropping Patterns of Annual Crops: A Remote Sensing Review

2022

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Cropping patterns are defined as the sequence and spatial arrangement of annual crops on a piece of land. Knowledge of cropping patterns is crucial for crop production and land-use intensity. While cropping patterns are related to crop production and land use intensity, they are rarely reported in agricultural statistics, especially those relating to small farms in developing countries. Remote sensing has enabled mapping cropping patterns by monitoring crops’ spatial and temporal dynamics. In this paper, we reviewed remote sensing studies of single, sequential and intercropping patterns of annual crops practiced at local and regional scales. A total of 89 studies were selected from 753 publications based on their cropping pattern types and relevance to the scope of this review. The review found that despite the increase in single cropping pattern studies due to the Sentinel missions, studies on intercropping patterns are rare, suggesting that mapping intercropping is still challenging. More so, microwave remote sensing for mapping intercropping has not been fully explored. Given the complexities in mapping intercropping, our review highlights how less frequently used vegetation indices (VIs) that benefit from red-edge and SWIR spectral bands may improve intercropping mapping.

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Section: Mapping Intercropping Patternssupporting

confidence: 82%

Section: Mapping Intercropping Patternsmentioning

confidence: 99%

Cropping Patterns of Annual Crops: A Remote Sensing Review

2022

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“…The images obtained through Landsat 8 OLI have low resolution and a pan-sharpening technique is applied to calculate the vegetation indices [33][34][35]. The multispectral and hyperspectral images acquired through remote sensing were used for monitoring seasonally variable crop and soil status features such as crop diseases, crop biomass, the nitrogen content in leaves, weed and insect penetration, chlorophyll levels of leaves, moisture content, surface roughness, soil texture and soil temperature.…”

Section: Crop Image Acquisition By Remote Sensingmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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“…These components can include, water cover, land cover, crop cover, type of crop, urban cover, etc. A wide variety of such classification models are proposed by researchers [1,2,3], and each of them vary in terms of classification accuracy, application, computational complexity, delay needed for classification, etc. Survey of such models, along with their nuances, advantages, limitations, and future research scopes is discussed in the next section of this text, which will assist readers to identify currently best performing models for stratification of satellite images.…”

Section: Introductionmentioning

confidence: 99%

Design of a high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning for crop analysis

Dhande

Malik

2022

Preprint

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Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them have different data & process requirements. For instance, image capturing module Might obtain Images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters & dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed & discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type & severity of damage detection, with an average PSNR of 29.6 dB for different image types. The model was tested on different regions around our local geographical area, and consistent performance was observed. This performance was also compared with various state-of-the-art approaches, and it was observed that the proposed TRSATL model has 5% better accuracy, 4.6% better precision, and 7.9% better recall when compared with them, which makes it highly useful for real-time satellite-based crop classification applications.

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Agricultural Monitoring, an Automatic Procedure for Crop Mapping and Yield Estimation: The Great Rift Valley of Kenya Case

Cited by 29 publications

References 60 publications

Cropping Patterns of Annual Crops: A Remote Sensing Review

Cropping Patterns of Annual Crops: A Remote Sensing Review

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Design of a high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning for crop analysis

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