AIMC Topic: Agriculture

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Evaluation of deep learning and transform domain feature extraction techniques for land cover classification: balancing through augmentation.

Environmental science and pollution research international
The identification of features that can improve classification accuracy is a major concern in land cover classification research. This paper compares deep learning and transform domain feature extraction techniques for land cover classification of SA...

Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks.

Environmental science and pollution research international
Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the c...

A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Sensors (Basel, Switzerland)
Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to...

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis.

PloS one
With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of dat...

Suitability Evaluation of Crop Variety via Graph Neural Network.

Computational intelligence and neuroscience
With the continuous growth of the global population, insufficient food production has become an urgent problem to be solved in most countries. At present, using artificial intelligence technology to improve suitability between land and crop varieties...

A real-time object detection model for orchard pests based on improved YOLOv4 algorithm.

Scientific reports
Accurate and efficient real-time detection of orchard pests was essential and could improve the economic benefits of the fruit industry. The orchard pest dataset, PestImgData, was built through a series of methods such as web crawler, specimen image ...

Simulation of Transmission System of Crawler Self-propelled Rotary Tiller Based on Deep Learning.

Computational intelligence and neuroscience
Because of its good performance, crawler-type running gear plays a very important role in the fields of modern agriculture. This article aims to study the construction of the drive system of the crawler self-propelled rotary tiller with the deep lear...

Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.

Sensors (Basel, Switzerland)
Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the ...

Vision-Based Module for Herding with a Sheepdog Robot.

Sensors (Basel, Switzerland)
Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as t...

Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning.

Computational intelligence and neuroscience
Agriculture is a basic and pillar industry. With the integration and development of Internet+, platform economy, and various industries, the business model of agriculture-related platforms is also constantly innovating. In this context, it is necessa...