AI and Related Technologies in the Fields of Smart Agriculture: A Review

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 100 - 100

Published: Feb. 2, 2025

The integration of cutting-edge technologies—such as the Internet Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies—is revolutionizing agricultural practices, enhancing productivity, sustainability, efficiency. objective this study is to review literature regarding development evolution AI well other technologies in fields Agriculture they are developed transformed by integrating above technologies. areas examined open field smart farming, vertical indoor zero waste agriculture, precision livestock greenhouses, regenerative agriculture. This paper links current research, technological innovations, case studies present a comprehensive these being context for benefit farmers consumers general. By exploring practical applications future perspectives, work aims provide valuable insights address global food security challenges, minimize environmental impacts, support sustainable goals through application new

Language: Английский

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Harnessing artificial intelligence and remote sensing in climate-smart agriculture: the current strategies needed for enhancing global food security

Cogent Food & Agriculture, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 20, 2025

Global food security is seriously threatened by climate change, which calls for creative agricultural solutions. However, little known about how different smart technologies are integrated to enhance security. As a strategic reaction these difficulties, this review investigates the incorporation of remote sensing (RS) as well artificial intelligence (AI) into climate-smart agriculture (CSA). This demonstrates advances can improve resilience, productivity, and sustainability utilizing AI's capacity predictive analytics, crop modelling, precision agriculture, along with RS's strengths in projections, land management, continuous surveillance. Several important tactics were covered, such combining AI RS regulate risks, maximize resource utilization, practice choices. The also discusses issues like policy frameworks, building, accessibility that prevent from being widely adopted. highlights further CSA offers insights they help ensure systems remain secure changing climates.

Language: Английский

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Global distribution and sustainable management of Asian corn borer (ACB), Ostrinia furnacalis (Lepidoptera: Crambidae): recent advancement and future prospects

Bulletin of Entomological Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Jan. 21, 2025

Abstract The Asian corn borer (ACB), Ostrinia furnacalis (Guenée, 1854), is a serious pest of several crops, particularly destructive maize and other cereals throughout most Asia, including China, the Philippines, Indonesia, Malaysia, Thailand, Sri Lanka, India, Bangladesh, Japan, Korea, Vietnam, Laos, Myanmar, Afghanistan, Pakistan Cambodia. It has long been known as in South-east Asia invaded parts Solomon Islands, Africa certain regions Australia Russia. Consequently, worldwide efforts have increased to ensure new control strategies for O. management. In this article, we provide comprehensive review ACB covering its (i) distribution (geographic range seasonal variations), (ii) morphology ecology (taxonomy, life-history, host plants economic importance) (iii) management (which include agroecological approaches, mating disruption, integrated genetic chemical well biological control). Furthermore, conclude with recommendations some suggestions improving eco-friendly enhance sustainable infested areas.

Language: Английский

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Challenges and constraints in implementing integrated pest management for pepper stem borer (Lophobaris piperis Marshall) among Indonesian smallholder farmers: a critical review

Journal of Integrated Pest Management, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 1, 2025

Abstract This review examines the challenges that prevent adoption of integrated pest management in black pepper (Piper nigrum L.) cultivation Indonesia, emphasizing impact Lophobaris piperis Marshall (Coleoptera: Curculionidae), a critical stem borer Southeast Asian pepper-producing countries. The recommended strategies involve employing varieties tolerant to L. pipperis, routine population monitoring, mechanical controls, adherence adequate agricultural practices, and environmentally responsible pesticide management. encompasses technical nontechnical aspects, addressing like farmer skills, knowledge, government support, market prices. We identified obstacles opportunities implementing sustainable strategies, especially largest plantations Indonesia. comprehensive provides valuable insights for enhancing effectiveness sustainability management, ultimately benefiting smallholder farmers’ livelihoods their farming enterprises.

Language: Английский

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Information and communication technologies in sustainable crop protection: advancing towards sustainable agriculture

DELETED, Journal Year: 2025, Volume and Issue: unknown

Published: May 3, 2025

Language: Английский

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Insect Resistance to Insecticides: Causes, Mechanisms, and Exploring Potential Solutions

Archives of Insect Biochemistry and Physiology, Journal Year: 2025, Volume and Issue: 118(2)

Published: Feb. 1, 2025

ABSTRACT Insecticides play a crucial role as the primary means of controlling agricultural pests, preventing significant damage to crops. However, misuse these insecticides has led development resistance in insect pests against major classes chemicals. The emergence poses serious threat, especially when alternative options for crop protection are limited farmers. Addressing this challenge and developing new, effective, sustainable pest management approaches is not merely essential but also critically important. In absence solutions, understanding root causes behind insects becomes critical necessity. Without understanding, formulation effective combat remains elusive. With playing vital global food security public health, mitigating paramount. Given growing concern over insecticides, review addresses research gap by thoroughly examining causes, mechanisms, potential solutions. examines factors driving resistance, such evolutionary pressure excessive pesticide use, provides detailed analysis including detoxifying enzyme overproduction target site mutations. Providing an it discusses integrated management, strategic insecticide rotation, use new control technologies biological agents. Emphasizing urgency multifaceted approach, concise roadmap guiding future applications.

Language: Английский

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Advanced Insect Detection Network for UAV-Based Biodiversity Monitoring

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 962 - 962

Published: March 9, 2025

The Advanced Insect Detection Network (AIDN), which represents a significant advancement in the application of deep learning for ecological monitoring, is specifically designed to enhance accuracy and efficiency insect detection from unmanned aerial vehicle (UAV) imagery. Utilizing novel architecture that incorporates advanced activation normalization techniques, multi-scale feature fusion, custom-tailored loss function, AIDN addresses unique challenges posed by small size, high mobility, diverse backgrounds insects images. In comprehensive testing against established models, demonstrated superior performance, achieving 92% precision, 88% recall, an F1-score 90%, mean Average Precision (mAP) score 89%. These results signify substantial improvement over traditional models such as YOLO v4, SSD, Faster R-CNN, typically show performance metrics approximately 10–15% lower across similar tests. practical implications AIDNs are profound, offering benefits agricultural management biodiversity conservation. By automating classification processes, reduces labor-intensive tasks manual enabling more frequent accurate data collection. This collection quality frequency enhances decision making pest conservation, leading effective interventions strategies. AIDN’s design capabilities set new standard field, promising scalable solutions UAV-based monitoring. Its ongoing development expected integrate additional sensory real-time adaptive further applicability, ensuring its role transformative tool monitoring environmental science.

Language: Английский

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Federated LeViT-ResUNet for Scalable and Privacy-Preserving Agricultural Monitoring Using Drone and Internet of Things Data

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 928 - 928

Published: April 10, 2025

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections broad-spectrum pesticide application are inefficient, time-consuming, dangerous. New drone photography IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, heterogeneity, privacy make it hard to conclude these data. This study proposes lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency LeViT transformers ResUNet’s exact pixel-level segmentation address issues. The system uses multispectral footage sensor identify real-time insect hotspots, health, yield prediction. dynamic relevance sparsity-based feature selector (DRS-FS) improves ranking reduces redundancy. Spectral normalization, spatial–temporal alignment, dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using preserve capture regional trends. A huge, open-access dataset from varied environmental circumstances was used simulation experiments. suggested approach on conventional models ResNet, DenseNet, vision transformer 98.9% classification accuracy 99.3% AUC. scalable sustainable privacy-preserving precision because its high generalization, low latency, communication efficiency. lays groundwork real-time, intelligent monitoring systems in diverse, resource-constrained farming situations.

Language: Английский

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RicePest-DETR: A transformer-based model for accurately identifying small rice pest by end-to-end detection mechanism

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110373 - 110373

Published: April 17, 2025

Language: Английский

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Integrating weight and imaging features: A machine learning framework for larval instar identification in Mythimna separata (Walker)

Bulletin of Entomological Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10

Published: April 23, 2025

Abstract The oriental armyworm, Mythimna separata (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges timely and accurate monitoring, as conventional methods are labour-intensive prone to errors. To address these limitations, this study investigates the use of machine learning automated precise identification M. instars. A total 1577 images representing different instar were analysed geometric, colour, texture features. Additionally, weight was predicted using 13 regression models. Instar conducted Support Vector Classifier (SVC), Random Forest, Multi-Layer Perceptron. Key feature contributing classification accuracy subsequently identified through permutation importance analysis. results demonstrated potential automating with high efficiency accuracy. Predicted emerged key feature, significantly enhancing performance all Among tested approaches, BaggingRegressor exhibited best prediction ( R 2 = 98.20%, RMSE 0.2313), while SVC achieved highest (94%). Overall, integration other image-derived features proved be effective strategy. This demonstrates efficacy monitoring systems by providing scalable reliable framework management. proposed methodology improves efficiency, offering actionable insights implementing targeted biological chemical control strategies.

Language: Английский

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