From Conventional to Artificial Intelligence-Based Agriculture

個数：

予約

From Conventional to Artificial Intelligence-Based Agriculture

Sharma, Vivek (EDT)/ Salwan, Richa (EDT)/ Sharma, Rhydum (EDT)

ウェブストア価格 ¥39,177（本体¥35,616）
Academic Press Inc（2026/06発売）
外貨定価 US$ 175.00
ポイント 356pt

現在予約受付中です。出版後の入荷・発送となります。
（重要：表示されている発売日は予定となり、発売が延期、中止、生産限定品で商品確保ができないなどの理由により、ご注文をお取消しさせていただく場合がございます。予めご了承ください。）

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 230 p.
言語 ENG
商品コード 9780443274657

Full Description

From Conventional to Artificial Intelligence-Based Agriculture explores the evolving landscape of agriculture as it transitions from traditional practices to advanced, AI-driven solutions. With AI and machine learning revolutionizing industries worldwide, their impact on agriculture is becoming increasingly significant. These technologies are not only aiding in climate modeling but also opening new possibilities for precision farming, enabling more accurate crop health diagnostics, efficient resource management, and timely intervention strategies.

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.

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