Machine Learning for Precision Agriculture and Crop Yield Optimization DOI

Prodipto Roy,

Mrutyunjay Padhiary,

Azmirul Hoque

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 189 - 234

Published: March 28, 2025

The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways addressing complex challenges related to modern farming. This chapter discusses the use ML in precision emphasizing its capacity maximize crop output and improve agricultural practices. It studies supervised, unsupervised, reinforcement, deep methodologies evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, equipment. Principal applications include predictive modeling for yield estimation, pest disease identification, health assessment, irrigation optimization, fertilization. also examines problems limits implementation data quality farmer acceptance.

Language: Английский

Towards Climate-Smart Agriculture: Strategies for Sustainable Agricultural Production, Food Security, and Greenhouse Gas Reduction DOI Creative Commons
Wogene Kabato, Girma Tilahun Getnet, Tamrat Sinore

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 565 - 565

Published: Feb. 25, 2025

Without transformative adaptation strategies, the impact of climate change is projected to reduce global crop yields and increase food insecurity, while rising greenhouse gas (GHG) emissions further exacerbate crisis. While agriculture a major contributor through unsustainable practices, it also offers significant opportunities mitigate these adoption sustainable practices. This review examines climate-smart (CSA) as key strategy for enhancing productivity, building resilience, reducing GHG emissions, emphasizing need strategic interventions accelerate its large-scale implementation improved security. The analysis revealed that nitrogen use efficiency (NUE) has in developed countries, NUE remains at 55.47%, precision nutrient management integrated soil fertility strategies enhance productivity minimize environmental impacts. With 40% world’s agricultural land already degraded, sustainability alone insufficient, necessitating shift toward regenerative practices restore degraded water by improving health, biodiversity, increasing carbon sequestration, thus ensuring long-term resilience. CSA including agriculture, biochar application, agroforestry, improve security, emissions. However, result variability highlights site-specific optimize benefits. Integrating multiple enhances health more effectively than implementing single practice alone. Widespread faces socio-economic technological barriers, requiring supportive policies, financial incentives, capacity-building initiatives. By adopting technologies, can transition sustainability, securing systems addressing challenges.

Language: Английский

Citations

5
Exploring the Role of Nature-based Solutions and Emerging Technologies in Advancing Circular and Sustainable Agriculture: An Opinionated Review for Environmental Resilience DOI Creative Commons
Eliakira Kisetu Nassary

Cleaner and Circular Bioeconomy, Journal Year: 2025, Volume and Issue: unknown, P. 100142 - 100142

Published: Feb. 1, 2025

Language: Английский

Citations

0
Reduction of microbiological contamination of poultry feed by electrophysical method DOI Creative Commons

Atkham Borotov,

Akmal Allanazarov,

Dilshod Baratov

et al.

BIO Web of Conferences, Journal Year: 2025, Volume and Issue: 161, P. 00068 - 00068

Published: Jan. 1, 2025

The problem of microbial contamination feed negatively affects the efficiency and safety livestock production. In this study, level various feeds for poultry was evaluated effectiveness electrophysical method its reduction studied. objects study were compound laying hens, plant-based mix spring wheat grain. selected samples examined presence total count (TMC), fungal (TFC), as well Salmonella Escherichia coli. experimental microwave treated on a specialised processing line at 60 kW power, 915 MHz frequency 90 seconds exposure. Analyses showed that initial varied with type, no or E. coli detected in control samples. Microwave treatment resulted significant WMB WBC counts all types tested. obtained results confirm EMF application to reduce feeds.

Language: Английский

Citations

0
Machine Learning for Precision Agriculture and Crop Yield Optimization DOI

Prodipto Roy,

Mrutyunjay Padhiary,

Azmirul Hoque

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 189 - 234

Published: March 28, 2025

The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways addressing complex challenges related to modern farming. This chapter discusses the use ML in precision emphasizing its capacity maximize crop output and improve agricultural practices. It studies supervised, unsupervised, reinforcement, deep methodologies evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, equipment. Principal applications include predictive modeling for yield estimation, pest disease identification, health assessment, irrigation optimization, fertilization. also examines problems limits implementation data quality farmer acceptance.

Language: Английский

Citations

0