AIMC Topic: Crops, Agricultural

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Helmets Labeling Crops: Kenya Crop Type Dataset Created via Helmet-Mounted Cameras and Deep Learning.

Scientific data
Accurate, up-to-date agricultural monitoring is essential for assessing food production, particularly in countries like Kenya, where recurring climate extremes, including floods and droughts, exacerbate food insecurity challenges. In regions dominate...

Chinese crop diseases and pests named entity recognition based on variational information bottleneck and feature enhancement.

Scientific reports
Chinese crop diseases and pests named entity recognition (CCDP-NER) is a critical step in extracting domain-specific information in the field of crop diseases and pests, playing a significant role in promoting agricultural informatization. To address...

Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition.

Scientific reports
The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is ch...

Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application.

Scientific reports
In Ethiopia, Teff is a vital staple crop, yet its productivity is significantly challenges due to inefficient weed and fertilizer management, threatening food security. Traditional weed control methods rely on manual labor and the indiscriminate appl...

A lightweight and explainable CNN model for empowering plant disease diagnosis.

Scientific reports
Crop disease is a significant challenge in agriculture, requiring quick and precise detection to safeguard yields and reduce economic losses. Traditional diagnostic methods are slow, labor-intensive, and rely on expert knowledge, limiting scalability...

A machine learning approach for estimating forage maize yield and quality in NW Spain.

PloS one
Crop models simulate crop growth and development according to different climatic, soil and crop management conditions. The CSM-CERES-Maize model (DSSAT) was adapted to simulate forage maize yields by calibrating the genetic parameters of six cultivar...

Recent Development of Methods and Techniques in the Detection of Mycotoxins in Agricultural Products.

Journal of agricultural and food chemistry
Mycotoxins are produced by fungi and possess cytotoxic properties that cause extensive cellular damage. Mycotoxins pose a significant threat to the harvesting and storage of crops as well as potential carcinogenic, teratogenic, and mutagenic risks to...

Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm.

Scientific reports
Weeds and crops contribute to a endless resistance for similar assets, which leads to potential declines in crop production and enlarged agricultural expenses. Conventional models of weed control like extensive pesticide use, appear with the hassle o...

Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction.

Scientific reports
Accurate scientific predicting of water requirements for protected agriculture crops is essential for informed irrigation management. The Penman-Monteith model, endorsed by the Food and Agriculture Organization of the United Nations (FAO), is current...

Study on the effect of light distribution on the greenhouse environment in Chinese solar greenhouse.

PloS one
Solar greenhouse is a primary agricultural facility in northern China during winter, providing a certain level of security for the demand for vegetables and melons in the northern regions. However, there remains a lack of uniformity between crop requ...