From Conventional to Artificial Intelligence-Based Agriculture
Editat de Vivek Sharma, Richa Salwan, Rhydum Sharmaen Limba Engleză Paperback – iun 2026
By integrating conventional agricultural knowledge with cutting-edge AI tools, farmers and researchers can better assess soil conditions, predict optimal planting windows, monitor nutrient dynamics, and understand market trends with greater precision. This convergence of tradition and technology supports more resilient, productive, and sustainable agricultural systems, paving the way for a smarter and more food-secure future.
- Reveals how artificial intelligence is transforming foundational agricultural practices
- Highlights emerging opportunities and challenges
- Provides insights for future research and development
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Specificații
ISBN-13: 9780443274657
ISBN-10: 0443274657
Pagini: 230
Dimensiuni: 191 x 235 mm
Greutate: 0.45 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0443274657
Pagini: 230
Dimensiuni: 191 x 235 mm
Greutate: 0.45 kg
Editura: ELSEVIER SCIENCE
Cuprins
1. Scope of conventional knowledge and deep learning approaches for the identification of plant diseases
2. Plant disease diagnosis and forecasting in the era of artificial intelligence, machine learning, and deep learning
3. AI-powered precision horticulture: Integrating machine learning and unmanned vehicles for crop management
4. Exploring conventional methods and deep learning approaches for plant disease identification
5. Machine learning and artificial intelligence for germplasm phenotyping in plant breeding
6. Potential of genome language models for plant genome mining in accelerating breeding strategies
7. Use of artificial intelligence in hydroponic vegetable production
8. Bibliometric analysis of artificial intelligence and machine learning: A technological revolution in agriculture
9. Soil health monitoring using artificial intelligence and the Internet of Things for sustainable agriculture
10. Generative AI and the potential of robotics in agriculture
11. Artificial intelligence in food science and nutrition
12. Artificial intelligence and machine learning in agriculture: Transforming economics and farm viability in the agricultural sector
2. Plant disease diagnosis and forecasting in the era of artificial intelligence, machine learning, and deep learning
3. AI-powered precision horticulture: Integrating machine learning and unmanned vehicles for crop management
4. Exploring conventional methods and deep learning approaches for plant disease identification
5. Machine learning and artificial intelligence for germplasm phenotyping in plant breeding
6. Potential of genome language models for plant genome mining in accelerating breeding strategies
7. Use of artificial intelligence in hydroponic vegetable production
8. Bibliometric analysis of artificial intelligence and machine learning: A technological revolution in agriculture
9. Soil health monitoring using artificial intelligence and the Internet of Things for sustainable agriculture
10. Generative AI and the potential of robotics in agriculture
11. Artificial intelligence in food science and nutrition
12. Artificial intelligence and machine learning in agriculture: Transforming economics and farm viability in the agricultural sector