The use of deep networks in agriculture has increased enormously in the last decade
including their use to classify different plant leaf stresses. More recently, a large number of …

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many
fields, including agriculture. This paper aims to shed light on deep learning techniques used …

Stress due to nutrients deficiency in plants can reduce the agricultural yield significantly.
Nitrogen, an essential nutrient, is a crucial growth-limiting factor and is the prime component …

Plant stress is one of major issues that cause significant economic loss for growers. The
labor-intensive conventional methods for identifying the stressed plants constrain their …

The agriculture sector is no exception to the widespread usage of deep learning tools and
techniques. In this paper, an automated detection method on the basis of pre-trained …

Biotic stress consists of damage to plants through other living organisms. The efficient
control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely …

In the modern era, deep learning techniques have emerged as powerful tools in image
recognition. Convolutional Neural Networks, one of the deep learning tools, have attained …

Deep learning is known as a promising multifunctional tool for processing images and other
big data. By assimilating large amounts of heterogeneous data, deep-learning technology …

Introduction Recently, plant disease detection and diagnosis procedures have become a
primary agricultural concern. Early detection of plant diseases enables farmers to take …

Plant diseases are a critical issue in the farming industry, and early identification is essential
for plant monitoring. The leaves of plants represent the majority of disease symptoms …