Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

Ojo, Adegboyega; Curry, Edward; Porwol, Lukasz

doi:10.24251/hicss.2020.115

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Examining AI in sustainable agriculture can be a potentially rich and important research stream in green IS (Nishant, Kennedy & Corbett, 2020), with implications for using and developing AI in other environmental and social contexts. While recognizing that well-developed knowledge around the design and use of IS undoubtedly has relevance to questions related to AI and other agricultural technologies (García Pereira, Ojo, Curry & Porwol, 2020;Ginige, Richards, Ginige & Hitchens, 2020;Newlands, Ghahari, Gel, Lyubchich & Mahdi,2019;Ofori & El-Gayar, 2020), we argue that the distinctive nature of AI necessitates new perspectives. Literature on the interaction between humans and AI (Tarafdar, Page, & Marabelli, 2022) suggests a continuous transformation of human knowledge (Grønsund & Aanestad, 2020;Raisch & Krakowski, 2020).…”

Section: Introductionmentioning

confidence: 87%

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

Lakshmi,

Corbett

2023

CAIS

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The world is confronted with the grand challenge of food insecurity amidst growing populations and the climate crisis. Artificial intelligence (AI) deployed in agricultural decision support systems (AgriDSS) raises both hopes and concerns for increasing agricultural productivity in sustainable ways. In this paper, we conduct a scoping review to uncover the roadblocks to the use of AI-supported AgriDSS in sustainable agriculture. Based on the corpus of 121 articles, we find that the effective use of AI-supported AgriDSS is hindered at technical, social, ethical, and ecological levels. Then, drawing on the experiential learning perspective, we propose how conjoint experiential learning (CEL) can enhance sustainable agricultural practices by enhancing both AI and human learning and overcoming roadblocks in using AgriDSS. Based on this conceptual framework, we build a research agenda that suggests blind spots and possible directions for future research.

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Section: Introductionmentioning

confidence: 87%

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

Lakshmi,

Corbett

2023

CAIS

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“…In the GIS context, deep learning models have been used for different purposes such as geospatial modelling, remotely sensed imagery processing, navigation, governance and societal, and agriculture. Specifically, the efficacy of one-dimensional convolutional neural networks over RF has been studied when exploiting the temporal and spectral dimensions of remotely sensed imagery in other parts of the world (García Pereira et al, 2020;Pereira et al, 2019Pereira et al, , 2021. Other studies have demonstrated the potential of Recurrent Neural Networks (RNNs) such as Long-Short Term Memory (LSTM) to classify multitemporal Synthetic Aperture Radar (Ienco et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

García Pereira,

Porwol,

Ojo

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modestsize, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative groundtruth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work.

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“…The combination of spatial science, artificial intelligence methods, data mining, and high-performance computing to extract information from big spatial data is known as geospatial artificial intelligence (GeoAI) (Gomez et al 2016, Li et al 2016, VoPham et al 2018. GeoAI opens up more possibilities for using earth observation data collected from various constellations of satellites and sensors with high spatial, spectral, and temporal resolution (Pereira 2020).…”

Section: Introductionmentioning

confidence: 99%

SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor)

Nuraeni,

Manessa

2023

IJReSES

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Indonesia's tea production and export volume have fluctuated with a downward trend in the last five years, partly due to the increasingly competitive world tea quality. Crop yield estimation is part of the management of tea plucking, affecting tea quality and quantity. The constraint in estimating crop yields requires technology that can make the process more effective and efficient. Remote sensing technology and machine learning have been widely used in precision agriculture. Recently, big data processing, especially remote sensing data, machine learning, and deep learning have been carried out using a cloud computing platform. Therefore, we propose using GeoAI, a combination of Sentinel-2A imagery, machine learning, and Google Collaboratory, to predict ready for plucking tea leaves at optimal plucking time at Gunung Mas Plantation Bogor. We used selected bands of Sentinel-2A and extracted more features (i.e., NDVI) as a training set. Then we utilized the tea blocks boundary and tea plucking data to generate labels using Random Forest (RF) and Support Vector Machine (SVM). The classification results were further used to estimate the production of crop tea yield. The RF classifier is able to achieve overall accuracy at 51% and SVM at 54%. Meanwhile, accuracy at optimally aged tea blocks is able to achieve at 75.62% for RF and 52.88% for SVM. Thus, the SVM classifier is better in terms of overall accuracy. Meanwhile, the RF classifier is superior in predicting ready for plucking tea at optimally aged tea blocks.

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Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

Cited by 7 publications

References 32 publications

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor)

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