Topic Review
Introducing New Index in Forest Roads Pavement ManagementSystem
Forest Road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. 
  • 12
  • 28 Sep 2022
Topic Review
Methods, Technologies, and Approaches to Monitoring PWS
Global climate change presents a threat for the environment, and it is aggravated by the mismanagement of water use in the agricultural sector. Since plants are the intermediate component of the soil–plant–atmosphere continuum, and their physiology is directly affected by water availability, plant-based approaches proved to be sensitive and effective in estimating plant water status and can be used as a possible water-saving strategy in crop irrigation scheduling. The plant water status (PWS) assessment is an approach that aims to help farmers elaborate an irrigation schedule as a possible water-saving strategy.
  • 35
  • 27 Sep 2022
Topic Review
UV-A Photocatalysis in Livestock and Poultry Farming
As the scale of the livestock industry has grown with the increase in the demand for livestock and poultry products, gaseous emissions, an unwanted side effect of livestock and poultry production, are also increasing. Various mitigation technologies have been developed to reduce such air pollution, and the mitigation technologies are divided mainly into “source-based type” (meant to fundamentally reduce the emissions) and “end-of-pipe type” (physicochemical and biological treatment of the output from barns to reduce the release into the environment). Ultraviolet light (UV) can be considered as both end-of-pipe (treating exhaust air from barns) and source-based type (treating air inside the barn).
  • 98
  • 20 Sep 2022
Topic Review
Machine Learning for Crop Diseases and Pests
Rapid population growth has resulted in an increased demand for agricultural goods. Pests and diseases are major obstacles to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. 
  • 43
  • 16 Sep 2022
Topic Review
End Effectors in Agricultural Robotic Harvesting Systems
An end effector is a peripheral device attached to a robot’s wrist, enabling interaction during a task. For harvesting robots, the end effector is considered the contact point between the robot and the product to be harvested. Robotic systems have been designed to cover labor shortage, to increase the speed of harvesting, and to improve the efficiency of harvesting.
  • 39
  • 30 Aug 2022
Topic Review
Ground Autonomous Vehicles for Agriculture
The available autonomous ground platforms developed by universities and research groups that were specifically designed to handle agricultural tasks was performed. As cost reduction and safety improvements are two of the most critical aspects for farmers, the development of autonomous vehicles can be of major interest, especially for those applications that are lacking in terms of mechanization improvements.
  • 184
  • 22 Aug 2022
Topic Review
Laser-Induced Breakdown Spectroscopy for Food Quality Evaluation
Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). 
  • 105
  • 28 Jul 2022
Topic Review
Deep Learning for Image Annotation in Agriculture
The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry.
  • 160
  • 27 Jul 2022
Topic Review
Robotics and Agriculture
The world’s population is steadily increasing, necessitating an increased food supply. According to Sylvestere, agricultural production, particularly field agriculture, must increase by 70% by 2050, when the global population is predicted to exceed 9 billion people. Simultaneously, increased agricultural activity leads to the waste and exploitation of irrigation water, fertiliser, and other phytosanitary products, compromising environmental sustainability and farmers’ profits. The employment of robots in agriculture appears to be a potential option in the context of precision agriculture since it enables repetitive labour to be accomplished without losing precision throughout the working day. This sort of application is growing in popularity in robotics research, and a variety of robots are now available for purchase. 
  • 104
  • 25 Jul 2022
Topic Review
Agricultural Big Data Architectures and Climate Change
Climate change is currently one of agriculture’s main problems in achieving sustainability. It causes drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that improve the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources.
  • 116
  • 06 Jul 2022
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