This research evaluates 20 advanced convolutional neural network (CNN) architectures for
classifying mushroom diseases in Agaricus bisporus, utilizing a custom dataset of 3195 …

Accurate peach detection is essential for automated agronomic management, such as
mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches …

The integration of cutting-edge precision agriculture technologies into avocado production is
a promising strategy to boost productivity and profitability in this thriving industry. While …

Multi-objective decision systems for the management, monitoring and control of complex
processes require the application of new methods and models of abstraction and formal …

Citrus cultivation is confronted with significant risks posed by a multitude of diseases, which
demand novel methodologies to ensure prompt and precise detection. The current research …

Citrus disease has a significant influence on agricultural productivity these days, so
technology based on artificial intelligence has been developed for creating computer vision …

Detecting and classifying apple fruit leaf diseases manually is a time-consuming and
expensive task, requiring expertise. This process not only affects the quality of the leaf …

Accurately segmenting crop and weed images in agricultural fields is crucial for precision
farming and effective weed management. This study introduces a new method that …

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer
vision that efforts to automatically recognise and detection of insect species utilizing visual …

Visual inspections by US Customs and Border Protection agriculture specialists have
identified approximately 20,000 regulated, quarantined pests each year in agricultural …