Researches & Trends

Brent Murphree,Senior Editor,Farm Press July 22, 2026https://www.farmprogress.com/technology/practical-applications-for-ai-in-farming-continue-developing AI is a powerful tool for farmers, but data security and recommendation reliability still require extra care.Dmytro Diedov/Getty Images At a Glance Artificial intelligence is becoming increasingly common on agricultural operations, and farmers can use it to assist in a variety of on-farm tasks. Aaron Smith, professor of

July 22, 2026https://www.usda.gov/about-usda/news/press-releases/2026/07/22/usda-asks-partners-develop-ai-solutions-accelerate-crop-innovation (Washington, D.C., July 22, 2026) – The U.S. Department of Agriculture (USDA) is taking steps to speed up progress in plant science by calling on universities and stakeholders to help build new AI tools that can translate the huge amount of germplasm data the agency collects. Through the Genesis Mission and the Agriculture

13 July 2026https://www.qfvg.com.au/news-room/from-drones-to-ai-harvesters-australian-pineapple-industrynbspshowcasesnbspthe-future-of-farming Artificial intelligence, drones, elite pineapple genetics, and sustainable farming practices will take centre stage when Australia’s pineapple industry gathers for the 2026 Pineapple Field Days in Hervey Bay from 30 – 31 July.  Hosted by Queensland Fruit & Vegetable Growers (QFVG), the annual event brings together growers, researchers, and industry partners to showcase the research, technology, and on-farm

Faiz Rahman Siddiqui / Jul 10, 2026, 23:48 ISThttps://timesofindia.indiatimes.com/city/kanpur/harness-ai-in-agriculture-to-help-boost-farmers-income-guv/articleshow/132318795.cms Kanpur:Stressing the need to improve the university’s national standing, governor and chancellor Anandiben Patel on Friday called upon scientists, faculty members and students of Chandra Shekhar Azad University of Agriculture and Technology (CSA) to work relentlessly to raise its ranking and ensure that innovations developed in

July 9, 2026 – Toronto, Ontario – Agriculture and Agri-Food Canadahttps://www.canada.ca/en/agriculture-agri-food/news/2026/07/government-of-canada-invests-in-artificial-intelligence-and-remote-sensing-for-climate-smart-agriculture.html News release July 9, 2026 – Toronto, Ontario – Agriculture and Agri-Food Canada Canadian farmers need innovative solutions to stay competitive, boost productivity, and address the challenges of climate change. Supporting the development of advanced technologies in agriculture will help ensure food security, create jobs, and position Canada as a

By Alexia Massoud, Staff Writer July 5, 2026https://www.expressnews.com/business/article/texas-utsa-swri-tech-agriculture-program-22309757.php Two San Antonio institutions are teaming up to train students to use cutting-edge technology in agriculture to tackle the growing demand for food amid a national labor shortage in the industry. The University of Texas at San Antonio and the nonprofit Southwest Research Institute have developed a 24-week program to teach undergraduate students

Ning Wang a, Hamid R. Parsaei b,Yali Ren c, 28 June 2026https://www.sciencedirect.com/science/article/abs/pii/S0925231226008660 Abstract This paper presents a novel Deep Q-Learning (DQL) framework for multi-robot navigation that addresses the critical problem of target congestion in swarm robotics systems. The framework employs a Centralized Training with Decentralized Execution (CTDE) paradigm, where a single Deep Q-Network (DQN) agent

ACM Computing Surveys, Volume 58, Issue 13, 22 June 2026https://dl.acm.org/doi/10.1145/3817444 Abstract The rapid advancement of unmanned aerial vehicle (UAV) swarm systems has enabled their deployment in large-scale applications such as disaster response, environmental monitoring, logistics, and communication networks. In these scenarios, effective scheduling and coordination of UAV swarms are critical for mission success, particularly under

25 June 2026https://www.nature.com/articles/s41598-026-45593-z Abstract The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput