Remote sensing is a useful tool for monitoring spatio-temporal variations of crop
morphological and physiological status and supporting practices in precision farming. In …

Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …

Recently, a great many deep convolutional neural network (CNN)-based methods have
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …

Automatic detection and analysis of rice crop diseases is widely required in the farming
industry, which can be utilized to avoid squandering financial and other resources, reduce …

Internet of Things (IoT)-based automation of agricultural events can change the agriculture
sector from being static and manual to dynamic and smart, leading to enhanced production …

There is a rapid increase in the adoption of emerging technologies like the Internet of Things
(IoT), Unmanned Aerial Vehicles (UAV), Internet of Underground Things (IoUT), Data …

This paper presents the results of the evaluation of five deep learning architectures for the
classification of soybean pest images. The performance of Inception-v3, Resnet-50, VGG-16 …

Disease diagnosis is one of the major tasks for increasing food production in agriculture.
Although precision agriculture (PA) takes less time and provides a more precise application …

Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and
effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor …

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have
led to significant progress in image processing. Since 2016, many applications for the …