Abnormal crops image data play crucial role in controlling crop diseases and pest for smart
agriculture. However, current agricultural image acquisition methods suffer from low-value …

Weeds are a major threat to crop production. Automated innovations for reducing herbicides
and labor needed for weeding have become a high priority for sustainable weed …

The rise of self-supervised learning (SSL) methods in recent years presents an opportunity
to leverage unlabeled and domain-specific datasets generated by image-based plant …

We train and compare the performance of two machine learning methods, a multi-variate
regression network and a ResNet-50-based neural network, to learn and forecast plant …

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 …

Plant species recognition is a primordial task that forms the basis of solving several plant-
related computer-vision problems such as disease detection or growth monitoring. However …

Automatically distinguishing different types of plant images is a challenging problem
relevant to both Botany and Computer Science disciplines. Plant identification at the species …

In addition to being extremely non-linear, modern machine learning problems require
millions if not billions of parameters to solve or at least to get a good approximation of the …