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...
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...
Weed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices. This study presents a comprehensive approach...
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and de...
BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot-spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasib...
Alfalfa (Medicago sativa) is known to release allelopathic substances to affect the germination and growth of other plants, which have the potential to be applied in controlling weeds. Green foxtail (Setaria viridis) and barnyardgrass (Echinochloa cr...
BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing informatio...
BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting...
BACKGROUND: Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy f...
BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop ...
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