Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Окт. 24, 2024

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in yield quality caused by these problems. In recent years, remote sensing (RS) areas has been prevailed over unmanned aerial vehicle (UAV)-based applications. Herein, using methods such as keyword co-contribution analysis author co-occurrence bibliometrics, we found out hot-spots of this field. UAV platforms equipped with various types cameras other advanced sensors, combined artificial intelligence (AI) algorithms, especially deep learning (DL) were reviewed. Acknowledging critical role comprehending pests, along their defining traits, provided a concise overview indispensable foundational knowledge. Additionally, some widely used traditional machine (ML) algorithms presented performance results tabulated form comparison. Furthermore, summarized monitoring techniques DL introduced application prediction classification. Take it step further, newest most concerned applications large language model (LLM) vision (LVM) also mentioned herein. At end review, comprehensively discussed deficiencies existing research challenges be solved, well practical solutions suggestions near future.

Язык: Английский

---

Analysing the potential of ChatGPT to support plant disease risk forecasting systems

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100824 - 100824

Опубликована: Фев. 1, 2025

Язык: Английский

---

Knowledge assimilation: Implementing knowledge-guided agricultural large language model

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113197 - 113197

Опубликована: Фев. 1, 2025

Язык: Английский

---

The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101787 - 101787

Опубликована: Март 1, 2025

Язык: Английский

---

Climate-Resilient Agriculture: Leveraging Language Models for Mitigation and Adaptation

Environmental earth sciences, Год журнала: 2025, Номер unknown, С. 357 - 382

Опубликована: Янв. 1, 2025

Язык: Английский

---

Fields of the Future: Digital Transformation in Smart Agriculture with Large Language Models and Generative AI

Computer Standards & Interfaces, Год журнала: 2025, Номер unknown, С. 104005 - 104005

Опубликована: Март 1, 2025

Язык: Английский

---

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

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 235, С. 110373 - 110373

Опубликована: Апрель 17, 2025

Язык: Английский

---

Visual large language model for wheat disease diagnosis in the wild

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 227, С. 109587 - 109587

Опубликована: Ноя. 1, 2024

Язык: Английский

---

Evaluating AI-Generated Responses from Different Chatbots to Soil Science-Related Questions

Soil Advances, Год журнала: 2025, Номер 3, С. 100034 - 100034

Опубликована: Янв. 31, 2025

Язык: Английский

---

Foundation Models in Agriculture: A Comprehensive Review

Agriculture, Год журнала: 2025, Номер 15(8), С. 847 - 847

Опубликована: Апрель 14, 2025

This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by need for efficient and intelligent decision support systems face growing global population climate change. It begins outlining development history FMs, including general FM training processes, application trends challenges, before focusing on Agricultural (AFMs). The examines diversity applications AFMs areas like crop classification, pest detection, image segmentation, delves into specific use cases such as agricultural knowledge question-answering, video analysis, support, robotics. Furthermore, it discusses challenges faced AFMs, data acquisition, efficiency, shift, practical challenges. Finally, future directions emphasizing multimodal applications, integrating across food sectors, decision-making systems, ultimately aiming to promote digitalization transformation agriculture.

Язык: Английский

---