A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly
and efficiently. Image-based, high-throughput phenotyping has a number of advantages …

Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides
valuable information about the structure and composition of an object. It has the ability to …

In agriculture, plant diseases are mainly accountable for reduction in productivity and leads
to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily …

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry
techniques are widely used for field-based, high-throughput plant phenotyping nowadays …

The increasing human population and variable weather conditions, due to climate change,
pose a threat to the world's food security. To improve global food security, we need to …

Convolutional neural networks (CNN) are increasingly being used to achieve state-of-the-art
performance for various plant phenotyping and agricultural tasks. While constructing such …

In recent years, deep learning models have become the standard for agricultural computer
vision. Such models are typically fine-tuned to agricultural tasks using model weights that …

Guava is one of the most important fruits in Pakistan, and is gradually boosting the economy
of Pakistan. Guava production can be interrupted due to different diseases, such as …

Introduction Machine learning tasks often require a significant amount of training data for the
resultant network to perform suitably for a given problem in any domain. In agriculture …

Cameras, sensors, and autonomous vehicles deployed in agricultural settings are producing
large, complex, and highly multidimensional datasets. Artificial intelligence techniques can …