  Modern scientific and technological advances allow botanists to use computer
vision-based approaches for plant identification tasks. These approaches have
their own challenges. Leaf classification is a computer-vision task performed
for the automated identification of plant species, a serious challenge due to
variations in leaf morphology, including its size, texture, shape, and
venation. Researchers have recently become more inclined toward deep
learning-based methods rather than conventional feature-based methods due to
the popularity and successful implementation of deep learning methods in image
analysis, object recognition, and speech recognition.
  In this paper, to have an interpretable and reliable system, a botanist's
behavior is modeled in leaf identification by proposing a highly-efficient
method of maximum behavioral resemblance developed through three deep
learning-based models. Different layers of the three models are visualized to
ensure that the botanist's behavior is modeled accurately. The first and second
models are designed from scratch. Regarding the third model, the pre-trained
architecture MobileNetV2 is employed along with the transfer-learning
technique. The proposed method is evaluated on two well-known datasets: Flavia
and MalayaKew. According to a comparative analysis, the suggested approach is
more accurate than hand-crafted feature extraction methods and other deep
learning techniques in terms of 99.67% and 99.81% accuracy. Unlike conventional
techniques that have their own specific complexities and depend on datasets,
the proposed method requires no hand-crafted feature extraction. Also, it
increases accuracy as compared with other deep learning techniques. Moreover,
SWP-LeafNET is distributable and considerably faster than other methods because
of using shallower models with fewer parameters asynchronously.
