Computer Vision in Self-Steering Tractors

Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation

Computer Vision in Self-Steering Tractors

Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation

A Hydrometallurgical Process for Cu Recovery from Printed Circuit Boards

The current study presents an effort to develop a sustainable hydrometallurgical process for the recovery of copper from waste printed circuit boards (PCBs) to be applied at local small to medium industrial units. The process aims to separate and recover copper from filter dust produced during the crushing of PCBs using a hammer mill in a recycling facility. Due to the high plastic content in the dust (approximately 30% w/w), the metal fraction was separated gravimetrically, and the material originated consisted mainly of Cu (23.8%), Fe (17.8%), Sn (12.7%), Pb (6.3%), Zn (3.4%), Al (3.3%), Mn (1.6%), and Ni (1.5%). Prior to copper recovery, the dust was leached with HCl as a pretreatment step. During this step, more than 80% of iron, zinc, and tin were leached out. The resulting solid consisted mainly of Cu (37.6%) and Fe (10.7%), leading to a copper enrichment of around 60% in the powder. The leaching of copper was conducted in a two-step process using H2SO4 as a leaching agent with the addition of H2O2 as an oxidizing agent. The experimental conditions had low energy requirements (no heating or agitation needed). The leaching of Cu reached 98%. Despite the pretreatment step, the concentration of other metals (Fe, Zn, Ni) in the pregnant solution was too high to proceed to electrowining. Therefore, the organic solvent ACORGA M5640 was selected for the extraction of copper from the pregnant solution. The extraction was conducted in two stages at pH equilibrium 1.5, and the loaded organic phase was stripped with HCl in two steps. The strip liquor was suitable for electrowinning

A Hydrometallurgical Process for Cu Recovery from Printed Circuit Boards

The current study presents an effort to develop a sustainable hydrometallurgical process for the recovery of copper from waste printed circuit boards (PCBs) to be applied at local small to medium industrial units. The process aims to separate and recover copper from filter dust produced during the crushing of PCBs using a hammer mill in a recycling facility. Due to the high plastic content in the dust (approximately 30% w/w), the metal fraction was separated gravimetrically, and the material originated consisted mainly of Cu (23.8%), Fe (17.8%), Sn (12.7%), Pb (6.3%), Zn (3.4%), Al (3.3%), Mn (1.6%), and Ni (1.5%). Prior to copper recovery, the dust was leached with HCl as a pretreatment step. During this step, more than 80% of iron, zinc, and tin were leached out. The resulting solid consisted mainly of Cu (37.6%) and Fe (10.7%), leading to a copper enrichment of around 60% in the powder. The leaching of copper was conducted in a two-step process using H2SO4 as a leaching agent with the addition of H2O2 as an oxidizing agent. The experimental conditions had low energy requirements (no heating or agitation needed). The leaching of Cu reached 98%. Despite the pretreatment step, the concentration of other metals (Fe, Zn, Ni) in the pregnant solution was too high to proceed to electrowining. Therefore, the organic solvent ACORGA M5640 was selected for the extraction of copper from the pregnant solution. The extraction was conducted in two stages at pH equilibrium 1.5, and the loaded organic phase was stripped with HCl in two steps. The strip liquor was suitable for electrowinning

Antimony Extraction from Galena Concentrates

The extraction of antimony and arsenic from galena concentrates by leaching with strongly alkaline sodium sulphide solution are investigated. The effects of leaching parameters including sodium sulphide and sodium hydroxide concentrations in the leaching solution, pulp density, reaction time and temperature on the extraction of antimony and arsenic are studied. It is indicated that high antimony extraction rates approaching 90–100% were obtained. However, arsenic extraction remained low at all experimental conditions considered, ranging between 2.5 and 4%, demonstrating that under these conditions, only certain arsenic-containing minerals are dissolved. The process presented is appropriate for antimony extraction with significant benefits associated with an increased value of galena concentrate and its own market value

Antimony Extraction from Galena Concentrates

The extraction of antimony and arsenic from galena concentrates by leaching with strongly alkaline sodium sulphide solution are investigated. The effects of leaching parameters including sodium sulphide and sodium hydroxide concentrations in the leaching solution, pulp density, reaction time and temperature on the extraction of antimony and arsenic are studied. It is indicated that high antimony extraction rates approaching 90–100% were obtained. However, arsenic extraction remained low at all experimental conditions considered, ranging between 2.5 and 4%, demonstrating that under these conditions, only certain arsenic-containing minerals are dissolved. The process presented is appropriate for antimony extraction with significant benefits associated with an increased value of galena concentrate and its own market value