Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

In the face of increasing agricultural demands and environmental concerns, the effective
management of weeds presents a pressing challenge in modern agriculture. Weeds not only …

The emergence of machine learning (ML) and deep learning (DL) techniques opens a huge
opportunity for their implementation in industry. One of the tasks for which these techniques …

Increasing marketability and waste management of agricultural products require quality
assessment. Meanwhile, their marketability is largely affected by their shapes and overall …

Infrastructure development is a leading driver of forest cover loss in the tropics, resulting in a
significant decrease in biodiversity. With recent advancements in digital image processing …

Image-based machine learning models can be used to make the sorting and grading of
agricultural products more efficient. In many regions, implementing such systems can be …

Today in the agricultural industry, many defects affect production efficiency; this paper aims
to show how the combination of machine vision (MV) and image processing (IP) helps to …

Identification and segregation of citrus fruit with diseases and peel blemishes are required to
preserve market value. Previously developed machine vision approaches could only …

Abstract Image classification using Convolutional Neural Network (CNN) achieves optimal
performance with a particular strategy. MobileNet reduces the parameter number for …

To scientifically and objectively monitor the fermentation quality of black tea, a computer
vision system (CVS) and electronic nose (e-nose) were employed to analyze the black tea …