Using Brainwave Patterns Recorded from Plant Pathology Experts to Increase the Reliability of AI-Based Plant Disease Recognition System.

One of the most challenging problems associated with the development of accurate and reliable application of computer vision and artificial intelligence in agriculture is that, not only are massive amounts of training data usually required, but also, in most cases, the images have to be properly labeled before models can be trained. Such a labeling process tends to be time consuming, tiresome, and expensive, often making the creation of large labeled datasets impractical. This problem is largely associated with the many steps involved in the labeling process, requiring the human expert rater to perform different cognitive and motor tasks in order to correctly label each image, thus diverting brain resources that should be focused on pattern recognition itself.

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps.

Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.

Counting Cattle in UAV Images-Dealing with Clustered Animals and Animal/Background Contrast Changes.

The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap.

A Study on the Detection of Cattle in UAV Images Using Deep Learning.

Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective.

The use of infrared images to detect ticks in cattle and proposal of an algorithm for quantifying the infestation.

This paper presents a study on the use of low resolution infrared images to detect ticks in cattle. Emphasis is given to the main factors that influence the quality of the captured images, as well as to the actions that can increase the amount of information conveyed by these images. In addition, a new automatic method for analyzing the images and counting the ticks is introduced.

An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing.

A method is presented to detect and quantify leaf symptoms using conventional color digital images. The method was designed to be completely automatic, eliminating the possibility of human error and reducing time taken to measure disease severity. The program is capable of dealing with images containing multiple leaves, further reducing the time taken.

Digital image processing techniques for detecting, quantifying and classifying plant diseases.

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. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey.