  Recently, the EAGL-I system was developed to rapidly create massive labeled
datasets of plants intended to be commonly used by farmers and researchers to
create AI-driven solutions in agriculture. As a result, a publicly available
plant species recognition dataset composed of 40,000 images with different
sizes consisting of 8 plant species was created with the system in order to
demonstrate its capabilities. This paper proposes a novel method, called
Variably Overlapping Time-Coherent Sliding Window (VOTCSW), that transforms a
dataset composed of images with variable size to a 3D representation with fixed
size that is suitable for convolutional neural networks, and demonstrates that
this representation is more informative than resizing the images of the dataset
to a given size. We theoretically formalized the use cases of the method as
well as its inherent properties and we proved that it has an oversampling and a
regularization effect on the data. By combining the VOTCSW method with the 3D
extension of a recently proposed machine learning model called 1-Dimensional
Polynomial Neural Networks, we were able to create a model that achieved a
state-of-the-art accuracy of 99.9% on the dataset created by the EAGL-I system,
surpassing well-known architectures such as ResNet and Inception. In addition,
we created a heuristic algorithm that enables the degree reduction of any
pre-trained N-Dimensional Polynomial Neural Network and which compresses it
without altering its performance, thus making the model faster and lighter.
Furthermore, we established that the currently available dataset could not be
used for machine learning in its present form, due to a substantial class
imbalance between the training set and the test set. Hence, we created a
specific preprocessing and a model development framework that enabled us to
improve the accuracy from 49.23% to 99.9%.
