AIMC Topic: Crops, Agricultural

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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...

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...

Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China.

Sensors (Basel, Switzerland)
Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier t...

Comparative Life Cycle Assessment of intra-row and inter-row weeding practices using autonomous robot systems in French vineyards.

The Science of the total environment
Viticulture, as well as other crops, is facing obligation to reduce the use of herbicides and to develop alternatives solutions to chemical weed control. These alternatives can be achieved by mechanical weeding either using tractors or weeding robots...

Deep learning-based approach for identification of diseases of maize crop.

Scientific reports
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop ...

Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN.

Sensors (Basel, Switzerland)
In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial ...

A deep learning model to detect novel pore-forming proteins.

Scientific reports
Many pore-forming proteins originating from pathogenic bacteria are toxic against agricultural pests. They are the key ingredients in several pesticidal products for agricultural use, including transgenic crops. There is an urgent need to identify no...

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Environmental monitoring and assessment
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...

A novel deep learning-based method for detection of weeds in vegetables.

Pest management science
BACKGROUND: Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at var...

Modeling risk of Sclerotinia sclerotiorum-induced disease development on canola and dry bean using machine learning algorithms.

Scientific reports
Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five ma...