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Agriculture is important in emerging nations like India, but food security is still a serious problem. Plant diseases, inadequate storage facilities, and poor transportation cause the majority of harvests to be squandered. Since illnesses cause ...

With the continuous development and application of Internet of Things technology, smart agriculture will become the trend of future agricultural development. Based on the current development needs of smart agriculture, a remote control system for smart ...

One of the most well-known names that has recently attained new heights and set a standard is Internet of Things (IoT). IoT aims to connect all physical devices in such a way that they are subject to human control over the Internet.The emergence ...

• A novel UAV-based method for efficient SCR monitoring.
• Green-red-red edge band related spectral indices are optimal features.
• Multi-view spectral measurements outperform single-view spectral measurements.
• 15 degree view has the ...

Southern corn rust (SCR) is a significant foliar disease, which can result in substantial corn yield losses. Unmanned aerial vehicle (UAV)-based optical remote sensing presents a promising method for efficiently monitoring SCR in field ...

• Research status of 3D reconstruction technology in agriculture was summarized.
• Sensors, systems and methods used in 3D reconstruction were summarized.
• The applications of 3D reconstruction methods in agriculture were summarized.

Perceiving the shape and structure of the real three-dimensional world through sensors and cameras is indispensable across various domains. The 3D reconstruction technology is dedicated to realizing this ideal process. 3D reconstruction ...

The Artificial Intelligence of Things (AIoT), a combination of the Internet of Things (IoT) and Artificial Intelligence (AI), plays an increasingly important role in smart agriculture (SA). AIoT has been adopted in many applications including ...

The recent technological advances and their applications to agriculture provide leverage for the new paradigm of smart agriculture. Remote sensing applications can help optimise resources, making agriculture more ecological, increasing ...

• A convolutional deep learning model was developed for anomaly detection in agricultural images.
• High and low-resolution imagery can be used as input for the model.
• Data transformation and augmentation are part of our methodology.

The past decade has witnessed the rapid development and adoption of machine and deep learning (ML & DL) methodologies in agricultural systems, showcased by great successes in applications such as smart crop management, smart plant breeding, smart ...

• Basics of large language and foundation models.
• Review of potential applications of large language and foundation models in agriculture.
• Outline challenges and opportunities.

Accurate detection of pests is vital in smart agriculture as it is among the main factors that profoundly influence the yield and quality of crops. In the actual field, pests frequently manifest as small objects, thereby presenting a considerable ...

• The effective utilization of plant context information by the adaptive feature fusion.
• The adequate design of pest detection model by the evolutionary neural architecture search.
• The detection accuracy of eleven field pests is ...

Artificial intelligence (AI) is driving the transformation and upgrading of traditional agriculture towards digitization and intelligence, improving agricultural efficiency and structural optimization. The agricultural environment is dynamic, with ...

Recently, Machine Learning (ML) has been heralded as a panacea for modelling problems across many domains, including Smart Agriculture (SmartAg), often in opposition to traditional mechanistic models arising on decades of scientific discovery. ...

• Mechanistic and machine learning models can be combined into a hybrid system to successfully predict out-of-distribution test instances the machine learning model would normally fail on for more trustworthy deployment.
• Mechanistic and ...

• • Flexible multimodal sensing system for life and health detection of smart fisheries.
• • A flexible multimodal sensing mechanism for smart fisheries health parameter detection is analyzed.
• • The parameter crosstalk and decoupling ...

Flexible sensors are widely used in human and animal health assessment due to their excellent biocompatibility and stretchability. Fisheries health assessment for complex application scenarios usually requires expensive instruments for analysis, ...

• Quantum computing is introduced, and differences with conventional computing are discussed.
• Other applications of Quantum technologies are included, with emphasis on AI and IoT.
• The expected applications and impact of Quantum ...

Agriculture has undergone progressive transformations using ever-evolving technologies to increase productivity and profitability. A new approach to agricultural management based on concepts from the fourth industrial revolution is being ...

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large ...

• Noisy IoT sensor data in evapotranspiration prediction are addressed.
• This research analyzes the impact of different types of noise in the data.
• Regression noise filters are proposed as a tool to improve data quality.
• The ...

A new idea called "smart agriculture" enables farms to be managed using cutting-edge technology like IoT, AI, robotics, drones, etc. The goal is to maximize the use of the resources at hand while producing more crops of higher quality and ...

• Reconstructing a 3D model of a grape bunch with uniquely identified berries from videos captured of a real grape field.
• New clustering and neural network-based for uniquely identifying berries in 3D point cloud and refined with video ...

A crucial step in the production of table grapes is berry thinning. This is because the market value of table grape production is significantly influenced by bunch compactness, bunch form and berry size, all of which are primarily regulated by ...

• This paper presents a novel one-shot domain adaptive real-time 3D detection method that addresses the challenge of insufficient target domain samples by designing a semantic-geometry-intensity space generator. This generator learns both ...

By introducing deep learning, LiDAR-based solutions have achieved impressive accuracy in 3D obstacle detection. However, gathering and labeling sufficient samples is the precondition for the effectiveness of existing solutions. This precondition ...

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple ...

• New real-time tech for automatic berry counting aids farmers in thinning.
• Novel method to predict berry number from a single 2D image.
• Design and implementation of 8 key features from a compact deep learning model.
• Achieves low ...

The advent of smart agriculture has revolutionized and streamlined various manual tasks in grape cultivation, one of which is berry thinning. This task necessitates experienced farmers to selectively remove a specific number of berries from the ...

• Empowers novice farmers with deep learning and augmented reality for grape berry thinning.
• Tested across the full growth cycle in an actual table grape field.
• The system elevates product quality by 8.18 % compared to skilled ...

Berry thinning is a crucial process in table grape cultivation. Such visual features as bunch compactness, bunch form, and berry size are important factors affecting market value. Moreover, sufficient space for each berry to grow also largely ...