UAV Remote Sensing Assessment of Crop Growth

Dorbu, Freda Elikem; Hashemi-Beni, Leila; Karimoddini, Ali; Shahbazi, Abolghasem

doi:10.14358/pers.21-00060r2

Cited by 8 publications

(6 citation statements)

References 44 publications

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“…Drones offer to farmers detailed, high-resolution images of crops, soil conditions, and other factors affecting crop growth. These images enable farmers to swiftly and accurately identify problems such as nutrient deficiencies (Onyango et al, 2021), pests, and diseases (Yu et al, 2022), allowing of timely corrective measures and avoiding crop losses (Maimaitijiang et al, 2020;Dorbu et al, 2021;Hara et al, 2021). The ability to detect such issues at an early stage is crucial for optimizing yields and ensuring agricultural sustainability (Xie & Yang, 2020).…”

Section: Precision Agriculture and Dronesmentioning

confidence: 99%

“…Moreover, drones provide farmers with comprehensive information on crop health and growth patterns (Pessi et al, 2020;Dorbu et al, 2021;Matthews, 2021). Equipped with sensors like infrared cameras, drones can detect variations of plant vigor, biomass, and other indicators of crop health (Maimaitijiang et al, 2020;Xie & Yang, 2020;Mesquita et al, 2021).…”

Section: Precision Agriculture and Dronesmentioning

confidence: 99%

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Revolutionizing agriculture from the skies: exploring the potential of spraying drones in precision farming

Castaldo

2023

CCT

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Este artigo analisa o modo como a agricultura está evoluindo e adotando novas tecnologias para atingir elevada eficiência. A agricultura de precisão é um fator-chave para esta evolução, e o uso de drones é identificado como uma das ferramentas que contribuem para a agricultura de precisão, particularmente em áreas onde o custo de mão de obra é alto, em áreas de difícil acesso e em pequenas fazendas. O artigo foca especificamente no uso dos drones de pulverização e seus benefícios, como menor custo e pulverização mais rápida do que os de pulverizadores tratorizados, acesso à culturas que são difíceis de se atingir, e habilidade de serem programados para voar em momentos específicos. O artigo também aborda as limitações dos drones de pulverização, tais como sua baixa capacidade, que pode levar ao uso de altas pressões de trabalho, e o alto custo dos equipamentos para os pequenos produtores. O maior desafio dos drones de pulverização são suas baterias, que precisam ser leves, mas fortes o suficiente para suprir a operação do drone. De modo geral, o uso dos drones na agricultura é promissor para o aumento de eficiência e precisão, mas ainda há detalhes que precisam ser discutidos, para que eles se tornem mais acessíveis e adotados pelos produtores.

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Section: Precision Agriculture and Dronesmentioning

confidence: 99%

Section: Precision Agriculture and Dronesmentioning

confidence: 99%

Revolutionizing agriculture from the skies: exploring the potential of spraying drones in precision farming

Castaldo

2023

CCT

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“…Remote sensing has emerged as a dependable method for monitoring fields, utilizing UAVs, piloted aircraft, and satellites for data collection [4,5]. UAVs are becoming increasingly vital in agriculture, thanks to their ability to carry diverse sensors and provide high-resolution, cost-effective imaging [6].…”

Section: Introductionmentioning

confidence: 99%

Detection of Individual Crop and Canopy Delineation from UAV Imagery

Dorbu,

Hashemi-Beni

2024

Preprint

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Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

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“…Remote sensing technology has been widely used for the classification and mapping purposes in agriculture including soil properties (Coopersmith et al, 2014), classification of crop species (Grinblat et al, 2016), detection of crop water stress (Mehdizadeh et al, 2017;Dorbu et al, 2021), monitoring of weeds and crop diseases (Milioto et al, 2017), and mapping of crop yield (Ramos et al, 2017). Recently, unmanned aerial systems (UASs) have become effective platforms for crop and weeds monitoring due to their abilities to hover close to crops and weeds to acquire high-resolution imagery at a low cost.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery

Hashemi-Beni

Gebrehiwot

Karimoddini

et al. 2022

Front. Remote Sens.

Self Cite

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Weeds are among the significant factors that could harm crop yield by invading crops and smother pastures, and significantly decrease the quality of the harvested crops. Herbicides are widely used in agriculture to control weeds; however, excessive use of herbicides in agriculture can lead to environmental pollution as well as yield reduction. Accurate mapping of crops/weeds is essential to determine weeds’ location and locally treat those areas. Increasing demand for flexible, accurate and lower cost precision agriculture technology has resulted in advancements in UAS-based remote sensing data collection and methods. Deep learning methods have been successfully employed for UAS data processing and mapping tasks in different domains. This research investigate, compares and evaluates the performance of deep learning methods for crop/weed discrimination on two open-source and published benchmark datasets captured by different UASs (field robot and UAV) and labeled by experts. We specifically investigate the following architectures: 1) U-Net Model 2) SegNet 3) FCN (FCN-32s, FCN-16s, FCN-8s) 4) DepLabV3+. The deep learning models were fine-tuned to classify the UAS datasets into three classes (background, crops, and weeds). The classification accuracy achieved by U-Net is 77.9% higher than 62.6% of SegNet, 68.4% of FCN-32s, 77.2% of FCN-16s, and slightly lower than 81.1% of FCN-8s, and 84.3% of DepLab v3+. Experimental results showed that the ResNet-18 based segmentation model such as DepLab v3+ could precisely extract weeds compared to other classifiers.

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UAV Remote Sensing Assessment of Crop Growth

Cited by 8 publications

References 44 publications

Revolutionizing agriculture from the skies: exploring the potential of spraying drones in precision farming

Revolutionizing agriculture from the skies: exploring the potential of spraying drones in precision farming

Detection of Individual Crop and Canopy Delineation from UAV Imagery

Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery

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