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Agriculture

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A Horizon Scan to Support Chemical Pollution-Related Policymaking for Sustainable and Climate-Resilient Economies.

Environmental toxicology and chemistry
While chemicals are vital to modern society through materials, agriculture, textiles, new technology, medicines, and consumer goods, their use is not without risks. Unfortunately, our resources seem inadequate to address the breadth of chemical chall...

Construction of deep learning-based disease detection model in plants.

Scientific reports
Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in pla...

Estimating the common agricultural policy milestones and targets by neural networks.

Evaluation and program planning
The New Delivery Model, introduced by the 2023-2027 Common Agricultural Policy, shifts the focus of policy programming and design from a compliance-based approach to one based on performance. The objectives indicated in the national strategic plans a...

Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Environmental science and pollution research international
Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool f...

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton.

Sensors (Basel, Switzerland)
Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT technique...

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review.

Sensors (Basel, Switzerland)
As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The conv...

Weed Detection Using Deep Learning: A Systematic Literature Review.

Sensors (Basel, Switzerland)
Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this...

Research on the evaluation method of agricultural intelligent robot design solutions.

PloS one
BACKGROUND: At present, agricultural robots are produced in large quantities and used in agricultural planting, and the traditional agricultural model is gradually shifting to rely on the Internet of Things and sensors to accurately detect crop growt...

Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module.

Sensors (Basel, Switzerland)
Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a ...

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm ().

Sensors (Basel, Switzerland)
Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile b...