Related papers: Corn leaf detection using Region based convolution…
Soybean leaf disease detection is critical for agricultural productivity but faces challenges due to visually similar symptoms and limited interpretability in conventional methods. While Convolutional Neural Networks (CNNs) excel in spatial…
Agriculture is an essential industry in the both society and economy of a country. However, the pests and diseases cause a great amount of reduction in agricultural production while there is not sufficient guidance for farmers to avoid this…
Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning and computer vision have been…
Retrieving images from large and varied repositories using visual contents has been one of major research items, but a challenging task in the image management community. In this paper we present an efficient approach for region-based image…
In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that…
In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To…
The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture…
Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate…
Monitoring the responses of plants to environmental changes is essential for plant biodiversity research. This, however, is currently still being done manually by botanists in the field. This work is very laborious, and the data obtained…
We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of each table from heterogeneous document images. For table detection, we…
Plant leaf diseases pose a significant danger to food security and they cause depletion in quality and volume of production. Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet…
Monitoring of reforestation is currently being considerably streamlined through the use of drones and image recognition algorithms, which have already proven to be effective on colour imagery. In addition to colour imagery, elevation data…
Most weed species can adversely impact agricultural productivity by competing for nutrients required by high-value crops. Manual weeding is not practical for large cropping areas. Many studies have been undertaken to develop automatic weed…
The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a…
Addressing airport traffic jams is one of the most crucial and challenging tasks in the remote sensing field, especially for the busiest airports. Several solutions have been employed to address this problem depending on the airplane…
Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a…
As an important research topic in computer vision, fine-grained classification which aims to recognition subordinate-level categories has attracted significant attention. We propose a novel region based ensemble learning network for…
Estimation of a single leaf area can be a measure of crop growth and a phenotypic trait to breed new varieties. It has also been used to measure leaf area index and total leaf area. Some studies have used hand-held cameras, image processing…
Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality. Early detection and classification of these diseases are essential for minimising losses and improving crop management practices. This…
In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations.…