Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
19,090
result(s) for
"Orchards"
Sort by:
Towards unmanned apple orchard production cycle : recent new technologies
This volume provides the most recent technology for sensing and automation in apple production cycle in terms of bagging robotics, flower pollination robotics, pruning robotics, and harvest robotics. It does not only summarise the development of technology progress, but also discuss the future trend for unmanned apple production cycle. Though apple production still mainly relies on manual labour, a huge number of innovative technologies emerge during the past years, which pave the road for unmanned apple orchard management.
Book
Mediterranean Olive Orchards under Climate Change: A Review of Future Impacts and Adaptation Strategies
The olive tree (Olea europaea L.) is an ancient traditional crop in the Mediterranean Basin. In the Mediterranean region, traditional olive orchards are distinguishable by their prevailing climatic conditions. Olive trees are indeed considered one of the most suitable and best-adapted species to the Mediterranean-type climate. However, new challenges are predicted to arise from climate change, threatening this traditional crop. The Mediterranean Basin is considered a climate change “hotspot,” as future projections hint at considerable warming and drying trends. Changes in olive tree suitability have already been reported over the last few decades. In this context, climate change may become particularly challenging for olive growers. The growing evidence for significant climate change in the upcoming decades urges adaptation measures to be taken. To effectively cope with the projected changes, both short and long-term adaptation strategies must be timely planned by the sector stakeholders and decision-makers to adapt for a warmer and dryer future. The current manuscript is devoted to illustrating the main impacts of climate change on olive tree cultivation in the Mediterranean Basin, by reviewing the most recent studies on this subject. Additionally, an analysis of possible adaptation strategies against the potentially negative impacts of climate change was also performed.
Journal Article
Fatima and the clementine thieves
\"One morning, Fatima and her grandfather wake up to find their clementine orchard savagely ransacked. Who could be doing this? How can the culprits be stopped? A little girl faces an ENORMOUS problem. Luckily, Fatima has powerful friends: the spiders!\"-- provided by publisher.
Book
Soil pollution indices and health risk assessment of metal(loid)s in the agricultural soil of pistachio orchards
Elevated levels of metal(loid)s in soil may pose potential threats to the ecosystem and can be harmful for human health. The concentrations of As, Cd, Pb, Cr and Ni were determined in agricultural soil collected from 45 pistachio orchards around Feizabad city, Khorasan Razavi province, Iran using ICP-OES. Also, soil pollution indices including contamination factor (CF), pollution load index (PLI) and geo-accumulation index (Igeo) were evaluated. In addition, non-carcinogenic and carcinogenic risk indices were estimated. The mean concentrations of metal(loid)s were in the order of Ni = 466.256 > Cr = 120.848 > Pb = 12.009 > As = 5.486 > Cd = 0.394 mg/kg. Concentrations of As, Cd and Pb in the soil samples were within their respective permissible limits set by World Health Organization (WHO). But concentrations of Cr and Ni in 84.4 and 100% of the samples, respectively exceeded the WHO allowable limits. The CF, PLI and Igeo showed that soil of some of the pistachio orchards was contaminated with some metals. The possible sources of the metals in the soil are application of pesticides, chemical fertilizers, manures as well as irrigation water. Hazard quotient (HQ) ad Hazard index (HI) values from soil of all the orchards were found to be well below the respective threshold limit (1), suggesting that there is no immediate non-cancer threat arising from the contamination at all the orchards with metal(loid)s for children and adults. The highest cancer risk values (1.13E-02 for children and 1.25E-03 for adults) were estimated for Ni in the soil. Collectively, this study provides valuable information to improve the soil in the pistachio orchards to reduce metal(loid)s contamination and minimize the associated health risks to the population in the area.
Journal Article
Apple countdown
Rhyming text describes a school field trip to an apple orchard, where the students count down all the things they see, from twenty nametags to one apple pie.
Book
The Plant Pathology Challenge 2020 data set to classify foliar disease of apples
Premise
Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts.
Methods and Results
We have manually captured 3651 high‐quality, real‐life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert‐annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine‐Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off‐the‐shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held‐out test set.
Discussion
This data set will contribute toward development and deployment of machine learning–based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert‐annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.
Journal Article