Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front
in terrestrial vegetation science, with emerging capability in space-based methodologies …

The development of hyperspectral remote sensing equipment, in recent years, has provided
plant protection professionals with a new mechanism for assessing the phytosanitary state of …

In this paper, we proposed a novel plant leaf disease identification model based on a deep
convolutional neural network (Deep CNN). The Deep CNN model is trained using an open …

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 …

Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be
useful in many agroforestry applications. However, it lacks the spectral range and precision …

Highlights•Hyperspectral imaging is an effective tool for in assessing quality
parameters.•The most abundantly used wavelengths are 601–850 nm, used in over 50% of …

Crop yields need to be improved in a sustainable manner to meet the expected worldwide
increase in population over the coming decades as well as the effects of anticipated climate …

Advances in automated and high-throughput imaging technologies have resulted in a
deluge of high-resolution images and sensor data of plants. However, extracting patterns …

Agriculture is the basis of every economy worldwide. Crop production is one of the major
factors affecting domestic market condition in any country. Agricultural production is also a …

During the last decade, there has been rapid adoption of ground and aerial platforms with
multiple sensors for phenotyping various biotic and abiotic stresses throughout the …