AgriSen - A Dataset for Crop Classification

Selea, Teodora; Pslaru, Marius-Florin

doi:10.1109/synasc51798.2020.00049

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…[5,23] Crop classification Crop classification aims to offer a global understanding of crop distribution and information for another application domain. [13,26,41] Disease and pest management Affect crop yields and quality and reduces resource use efficiency. The wide variety of weeds, animals, and microorganisms that threaten agricultural crops requires technology for their protection.…”

Section: Main Agriculture Domainsmentioning

confidence: 99%

Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends

Oliveira

Silva

2023

Applied Sciences

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The world’s population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture.

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Section: Main Agriculture Domainsmentioning

confidence: 99%

Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends

Oliveira

Silva

2023

Applied Sciences

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“…Many studies have utilized datasets containing images from satellites or UAVs since they are readily accessible. Overall, the datasets employed vary in size and complexity, offering a wide range of data for constructing and assessing crop classification models [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

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AgriSen - A Dataset for Crop Classification

Cited by 2 publications

References 14 publications

Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends

Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

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