Convolutional Neural Network Application in Smart Farming

Adhitya, Yudhi; Prakosa, Setya Widyawan; Köppen, Mario; Leu, Jenq‐Shiou

doi:10.1007/978-981-15-0399-3_23

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…These applications minimize cost, improve farmers' profit, enable faster and robust supply chain management, and improve transparency. Agricultural activities are driven by AI and ML models used for predicting future outcomes, making real-time operational decisions [62,63], and developing novel business models. In order to meet the requirement of sustainability, these agricultural business models also need to be sustainable.…”

Section: Sdg 2: Zero Hungermentioning

confidence: 99%

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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Section: Sdg 2: Zero Hungermentioning

confidence: 99%

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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“…Since the CNN algorithms need a lot of training data to perform [23], transfer learning has been used in a number of classification applications [24][25][26][27]. The learning methodologies have determined four kinds of transfer learning [28]: (1) instance-based transfer learning, (2) feature-based transfer learning, (3) model-based transfer learning, and (4) relation-based transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Sun

et al. 2022

Remote Sensing

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Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37%~95.25%.

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“…Future plans also include using data from several similar trials to predict harvest biomass using artificial intelligence techniques by training an appropriate convolutional neural network (CNN) [9] using descriptive statistics data derived from overhead images of the plants at appropriate growth stages. The project team also plans to conduct trials with two factors by varying both irrigation and nitrogen levels to study the impact on the growth of specialty vegetables and other medicinal plants in the future.…”

Section: Pitfalls and Successesmentioning

confidence: 99%

Education and Research at the Nexus of Food, Energy, and Water with a 3-Axis Farming Robot

Nagchaudhuri

Pandya

Mitra

et al.

2020 ASEE Virtual Annual Conference Content Access Proceedings

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Eastern Shore. He is a member American Society for Mechanical Engineers (ASME), American Society for Engineering Education (ASEE) and, American Society for Agricultural and Biological Engineers(ASABE) and is actively involved in teaching and research in the fields of (i) robotics and mechatronics, (ii)remote sensing and precision agriculture, and,(iii) biofuels and renewable energy. He has published more than 70 refereed articles in journals and conference proceedings. Dr. Nagchaudhuri received his baccalaureate degree from Jadavpur University in Kolkata, India with honors in Mechanical Engineering. Thereafter, he worked in a multinational industry for a little over three years before joining Tulane University as a graduate student in the fall of 1987. He received master's degree from Tulane University in 1989 and doctoral degree from Duke University 1992.

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Convolutional Neural Network Application in Smart Farming

Cited by 10 publications

References 19 publications

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Education and Research at the Nexus of Food, Energy, and Water with a 3-Axis Farming Robot

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