Highlights•The challenges involved in the automatic identification of plant diseases are
characterised.•The impact of those challenges on current proposals is discussed.•Possible …

This paper presents a survey on methods that use digital image processing techniques to
detect, quantify and classify plant diseases from digital images in the visible spectrum …

Leaf chlorophyll content (μg cm− 2) is an important variable for agricultural remote sensing
because of its close relationship to leaf N content. The objectives of this study were to …

Background The color of crop leaves is closely correlated with nitrogen (N) status and can
be quantified easily with a digital still color camera and image processing software. The …

Object recognition in depth images is challenging and persistent task in machine vision,
robotics, and automation of sustainability. Object recognition tasks are a challenging part of …

In recent years, agricultural image processing research has been a key emphasis. Image
processing techniques are used by computers to analyze images. New advancements in …

Nitrogen is an important variable for paddy farming management. The objectives of this
study were to develop and test a new method to determine the status of nitrogen and …

Non-destructive characterization of crop canopy is important to acquire timely and
inexpensive information for crop management and yield prediction. The objective of the …

A camera can record spectral information of visible bands. In this study, a digital camera was
used to take pictures of the canopies of 3 rice (Oryza sativa L.) cultivars with 6 different …

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer
application. The present study examined the ability of multiple image features derived from …