  Citrus juices and fruits are commodities with great economic potential in the
international market, but productivity losses caused by mites and other pests
are still far from being a good mark. Despite the integrated pest mechanical
aspect, only a few works on automatic classification have handled images with
orange mite characteristics, which means tiny and noisy regions of interest. On
the computational side, attention-based models have gained prominence in deep
learning research, and, along with weakly supervised learning algorithms, they
have improved tasks performed with some label restrictions. In agronomic
research of pests and diseases, these techniques can improve classification
performance while pointing out the location of mites and insects without
specific labels, reducing deep learning development costs related to generating
bounding boxes. In this context, this work proposes an attention-based
activation map approach developed to improve the classification of tiny regions
called Two-Weighted Activation Mapping, which also produces locations using
feature map scores learned from class labels. We apply our method in a
two-stage network process called Attention-based Multiple Instance Learning
Guided by Saliency Maps. We analyze the proposed approach in two challenging
datasets, the Citrus Pest Benchmark, which was captured directly in the field
using magnifying glasses, and the Insect Pest, a large pest image benchmark. In
addition, we evaluate and compare our models with weakly supervised methods,
such as Attention-based Deep MIL and WILDCAT. The results show that our
classifier is superior to literature methods that use tiny regions in their
classification tasks, surpassing them in all scenarios by at least 16
percentage points. Moreover, our approach infers bounding box locations for
salient insects, even training without any location labels.
