Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms DOI Creative Commons
Jorge Enrique Chaparro Mesa,

José Édinson Aedo,

Felipe Lumbreras

et al.

Journal of Agriculture and Food Research, Journal Year: 2024, Volume and Issue: 18, P. 101208 - 101208

Published: July 6, 2024

Nitrogen is the most important nutritional element during vegetative growth phase of pineapple crop; however, its presence in soil insufficient to meet plant demands. In this study, nine machine learning techniques were validated estimate total nitrogen (TN) content MD2 crops from data multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, moisture, wind speed and direction, well SPAD values indicating leaf chlorophyll content. Total taken tissue samples, then analyzed a laboratory. To introduce variability, complete randomized block experimental design was implemented, applying five different treatments blocks, each with 12 replications, 6-month period crop located Tauramena, Colombia. address inherent variability agricultural environmental data, dimensionality reduced using Principal Component Analysis (PCA). addition, regularization applied, including cross-validation, feature selection, boost methods, L1 (Lasso) L2 (Ridge) regularization, hyperparameter optimization. strategies generated more robust accurate models, multilayer perceptron regressor (MLP regressor) extreme gradient boosting (XGBoost) algorithms standing out. On first sampling date, XGBoost achieved R2 86.98 %, being highest. following dates, MLP 59.11 % second date; 68.00 third last 69.4 %. results indicate that integration use models could greatly improve precision nitro-gen (N) diagnostics crops, especially real-time applications. findings highlight promising potential developing integrate multisensor fusion for various applications agriculture.

Language: Английский

Machine learning in construction and demolition waste management: Progress, challenges, and future directions DOI
Yu Gao,

Jiayuan Wang,

Xiaoxiao Xu

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 162, P. 105380 - 105380

Published: March 16, 2024

Language: Английский

Citations

30
PND-Net: plant nutrition deficiency and disease classification using graph convolutional network DOI Creative Commons
Asish Bera, Debotosh Bhattacharjee, Ondřej Krejcar

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 5, 2024

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified detected at early stages. Hence, continuous health monitoring of is very crucial handling stress. The deep learning methods have proven its superior performances in the automated detection deficiencies from visual symptoms leaves. This article proposes a new method disease classification using graph convolutional network (GNN), added upon base neural (CNN). Sometimes, global feature descriptor might fail to capture vital region diseased leaf, which causes inaccurate disease. To address this issue, regional holistic aggregation. In work, region-based summarization multi-scales explored spatial pyramidal pooling discriminative representation. Furthermore, GCN developed capacitate finer details classifying insufficiency nutrients. proposed method, called Plant Nutrition Deficiency Disease Network (PND-Net), has been evaluated on two public datasets deficiency, four backbone CNNs. best PND-Net as follows: (a) 90.00% Banana 90.54% Coffee deficiency; (b) 96.18% Potato 84.30% PlantDoc Xception backbone. additional experiments carried out generalization, achieved state-of-the-art datasets, namely Breast Cancer Histopathology Image Classification (BreakHis 40

Language: Английский

Citations

17
Co-pyrolysis of biomass and plastic wastes and application of machine learning for modelling of the process: A comprehensive review DOI

Deepak Bhushan,

Sanjeevani Hooda,

Prasenjit Mondal

et al.

Journal of the Energy Institute, Journal Year: 2025, Volume and Issue: 119, P. 101973 - 101973

Published: Jan. 5, 2025

Language: Английский

Citations

4
Revolutionizing Thrust Manufacturing DOI

K. R. Senthilkumar

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 80 - 93

Published: March 28, 2024

This chapter explores the transformative impact of integrating real-time data and artificial intelligence (AI) in field thrust manufacturing, particularly within aerospace automotive industries. As manufacturing processes evolve, synergy between AI advancements emerges as a catalyst for unparalleled efficiency, precision, innovation. The examines foundational role providing granular view operations, complemented by sophisticated capabilities AI—from automation to adaptive intelligence. Through case studies, document showcases successful applications this optimizing production, predictive maintenance, quality control. Despite promise, challenges such security workforce upskilling are acknowledged. concludes envisioning future where convergence defines landscape intelligent presenting opportunities smart factories supply chains.

Language: Английский

Citations

14
Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm DOI

Leandra Schuastz Breda,

José Elton de Melo Nascimento, Vandressa Alves

et al.

Food Research International, Journal Year: 2024, Volume and Issue: 179, P. 113958 - 113958

Published: Jan. 10, 2024

Language: Английский

Citations

12
Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study DOI Open Access
Harbil Bediaga, Isabel Moreno, Sonia Arrasate

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(1), P. 121 - 121

Published: Jan. 6, 2025

Determining the values of various properties for new bio-inks 3D printing is a very important task in design materials. For this purpose, large number experimental works have been consulted, and database with more than 1200 bioprinting tests has created. These cover different combinations conditions terms print pressure, temperature, needle values, example. data are difficult to deal determining optimize analyze options. The best model demonstrated specificity (Sp) 88.4% sensitivity (Sn) 86.2% training series while achieving an Sp 85.9% Sn 80.3% external validation series. This utilizes operators based on perturbation theory complexity data. comparative purposes, neural networks used, similar results obtained. developed tool could easily be applied predict assays silico. findings significantly improve efficiency accuracy predictive models without resorting trial-and-error tests, thereby saving time funds. Ultimately, may help pave way advances personalized medicine tissue engineering.

Language: Английский

Citations

1
The role of automation and robotics in transforming hydroponics and aquaponics to large scale DOI Creative Commons
Milon Selvam Dennison, Pankaj Kumar, Fwangmun Wamyil

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 17, 2025

Language: Английский

Citations

1
Enhancing the accuracy of monitoring effective tiller counts of wheat using multi-source data and machine learning derived from consumer drones DOI

Ziheng Feng,

Jiaxiang Cai, Ke Wu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110120 - 110120

Published: Feb. 24, 2025

Language: Английский

Citations

1
Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview DOI Creative Commons
Mohamed Farag Taha, Hanping Mao, Zhao Zhang

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(6), P. 582 - 582

Published: March 9, 2025

Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt transition to Ag5.0, this paper comprehensively reviews role of AI, (ML) other emerging overcome current future crop management challenges. Crop has progressed significantly from early methods advanced capabilities marking notable leap precision agriculture. Emerging collaborative robots, 6G, digital twins, Internet Things (IoT), blockchain, cloud computing, quantum are central evolution. The also highlights how modern tools improving way we perceive, analyze, manage growth. Additionally, it explores real-world case studies showcasing application deep monitoring. Innovations smart sensors, AI-based communication systems driving next phase digitalization decision-making. addresses opportunities challenges that come with adopting emphasizing transformative potential these productivity tackling issues. Finally, is agriculture, highlight trends research needs multidisciplinary approaches, regional adaptation, advancements AI robotics. Ag5.0 paradigm towards management, fostering sustainable, data-driven farming optimize while minimizing environmental impact.

Language: Английский

Citations

1
Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review DOI Creative Commons
Ikechukwu Kingsley Opara, Umezuruike Linus Opara, Jude A. Okolie

et al.

Plants, Journal Year: 2024, Volume and Issue: 13(9), P. 1200 - 1200

Published: April 25, 2024

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production potential for predicting fresh produce losses waste. Recently, ML has been increasingly applied horticulture efficient accurate operations. Given health benefits need food nutrition security, postharvest management are important. This aims to assess application preharvest reducing waste by their magnitude, which is crucial practices policymaking loss reduction. starts assessing horticulture. It then presents handling processing, lastly, prospects its quantification. findings revealed that several algorithms perform satisfactorily classification prediction tasks. Based that, there a further investigate suitability more models or combination with higher prediction. Overall, suggested possible future directions related

Language: Английский

Citations

8