Crop signal markers facilitate crop detection and weed removal from lettuce and tomato by an intelligent cultivator (original) (raw)
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Crop signalling: A novel crop recognition technique for robotic weed control ScienceDirect
Weed control is a significant cost for speciality crop producers, especially on organic farms. Agricultural operations are still largely dependent on hand weeding that is labour intensive and labour shortages and rising wages have led to a surge in food production costs. Thus, there is an inherent need to automate weed control and contain both labour costs and demands. Automatically distinguishing weeds from the crop plant is a complex problem since weeds come in a wide variety of colours, shapes, and sizes, and crop plant foliage is often overlapped with itself or occluded by the weeds. Current technology in commercial use, cannot reliably and effectively perform the differentiation task in such complex scenarios in real-time. As a solution to this problem, our team at the University of California, Davis has developed a novel concept called crop signalling, a technology to make crop plants machine readable and reliably distinguishable from weeds for automatic weed control. Four different techniques have been investigated and developed to make smart crop marking systems such as a) systemic markers, b) fluorescent proteins, c) plant labels and d) topical markers. Indoor experiments have been conducted for each method. Field experiments, using plant labels and the topical markers methods, have been successfully conducted for real-time weed control in tomato and lettuce. The results demonstrated that robots could automatically detect and distinguish 99.7% of the crop plants with no false positive errors in dense complex outdoor scenes with high weed densities. The crop/weed differentiation was thus effective, fast, reliable, and commercialisation of robotic weed control using the technique may be feasible.
Crop signalling: A novel crop recognition technique for robotic weed control
Biosystems Engineering, 2019
Image processing Weed control is a significant cost for speciality crop producers, especially on organic farms. Agricultural operations are still largely dependent on hand weeding that is labour intensive and labour shortages and rising wages have led to a surge in food production costs. Thus, there is an inherent need to automate weed control and contain both labour costs and demands. Automatically distinguishing weeds from the crop plant is a complex problem since weeds come in a wide variety of colours, shapes, and sizes, and crop plant foliage is often overlapped with itself or occluded by the weeds. Current technology in commercial use, cannot reliably and effectively perform the differentiation task in such complex scenarios in real-time. As a solution to this problem, our team at the University of California, Davis has developed a novel concept called crop signalling, a technology to make crop plants machine readable and reliably distinguishable from weeds for automatic weed control. Four different techniques have been investigated and developed to make smart crop marking systems such as a) systemic markers, b) fluorescent proteins, c) plant labels and d) topical markers. Indoor experiments have been conducted for each method. Field experiments, using plant labels and the topical markers methods, have been successfully conducted for real-time weed control in tomato and lettuce. The results demonstrated that robots could automatically detect and distinguish 99.7% of the crop plants with no false positive errors in dense complex outdoor scenes with high weed densities. The crop/weed differentiation was thus effective, fast, reliable, and commercialisation of robotic weed control using the technique may be feasible.
Real-time weed-crop classification and localisation technique for robotic weed control in lettuce
Automatic weed control crop signalling robotics machine vision precision agriculture Robotic weed control for vegetables is necessary to increase crop productivity, avoid intensive hand weeding as labour shortages in developed countries such as United States has led to a surge in food production costs. However, development of a reliable, intelligent robotic system for weed control in real-time for vegetables still remains a challenging task. The main issue arises while distinguishing crops from weeds in real-time. In this paper, a novel technique to crop signalling to distinguish crops from in-row weeds in complex natural scenarios, such as high weed densities commonly found on organic farms, in real-time using machine vision is presented. Crop signalling is a simple and low-cost technique in which a signalling compound is produced by or applied to the crop and where the signalling compound is machine readable and helps to create visual features that uniquely distinguish the crops from weeds. The crop and weed mapping algorithm presented here were specially designed and developed for a vision-based weeding robot equipped with a micro-jet herbicide-spraying system for weed control in a lettuce field. The proposed technique involves weed/crop mapping and decision making. Experimental results show that the crop detection accuracy was 99.75%, and 98.11% of sprayable weeds were detected. The proposed technique is highly accurate, reliable and more robust than other sensor-based techniques presented in the literature.
Robotics Image processing Automated weed management tools in vegetable crops are needed to reduce or eliminate hand-weeding because of labour shortages and cost. Distinguishing crop plants from weeds in complex natural scenes of crop-weed mixtures remains a challenge for weed management automation. This paper presents a novel solution to the weed control problem by employing crop signalling technology: a novel systems approach that creates a machine-readable crop plant. A robot-vision-based weed-knife control system with a novel three-dimensional geometric detection algorithm was developed to automate weed control for tomato and lettuce crops. The system successfully detected the crop signal from occluded crop plants while traveling at speeds up to of 3.2 km h À1. The in-field experiments show that the system is able to reduce the number of weed plants by 83% in the seedling area. Crop detection accuracy was measured at 97.8% (precision 0.998 and recall 0.952) with a detection time of 30 ms f À1. This paper also shows that the crop signalling system has the advantage that prior knowledge of visual features of each crop and weed species is not required and poor visual appearance of the crop plants or weeds does not affect system performance.
Vision-based weed identification with farm robots
Kishore.ChandraSwain[a]agrsci.dk 1 M. Nørremark, Michael.Norremark[a]agrsci.dk 1 D. Bochtis, Dionysis.Bochtis[a]agrsci.dk 1 Claus Grøn Sørensen, claus.soerensen[a]agrsci.dk 1 O. Green, Ole.Green[a]agrsci.dk
The use of agricultural robots in weed management and control
Burleigh Dodds Series in Agricultural Science, 2019
Weed management and control are essential for the production of high-yielding and high-quality crops, and advances in weed control technology have had a huge impact on agricultural productivity. Any effective weed control technology needs to be both robust and adaptable. Robust weed control technology will successfully control weeds in spite of variability in the field conditions. Adaptable weed control technology has the capacity to change its strategy in the context of evolving weed populations, genetics, and climatic conditions. This chapter focuses on key work in the development of robotic weeders, including weed perception systems and weed control mechanisms. Following an extensive introduction, the chapter addresses the challenges of robotic weed control focusing on both perception systems, which can detect and classify weed plants from crop plants, and also weed control mechanisms, covering both chemical and mechanical weed control. A case study of an automated weeding system is provided. Disciplines Agricultural Economics | Agriculture | Bioresource and Agricultural Engineering | Robotics Comments This chapter is published as Steward, Brian, Jingyao Gai, and Lie Tang. "The use of agricultural robots in weed management and control. " In Robotics and automation for improving agriculture, edited by
Robotics-automation and sensor-based approaches in weed detection and control: A review
International Journal of Chemical Studies, 2020
Undesirable and unwanted plants grow autonomously, nonuniformly in farmland and compete with the beneficial crop called a weed. It strives with the crop for nutrients, sunlight, water, space and grows at a faster rate. This results in a decreased growth rate of crop seedlings, make them susceptible to pests and diseases, eventually responsible for crop yield reduction and pertains to the poor economic condition of farmers as well as the nation. Hence, weed control is very crucial in crop production. Several studies have documented the yield loss associated with weed competition. Limiting factors of general weed control methods create the situation for design-development of new approaches based on robotics, automation and sensor techniques. Many research studies documented various weed discrimination, identification and control mechanisms in the fields. The automatic distinction between crop-weed has its own importance in weed control applications. Sensor-based approaches, machine vision systems, RTK GPS based systems, etc. are found better to achieve effective weed control and helps in improving crop yield. Robotic technology could provide a means to reduce current dependency of agriculture on chemical herbicides, strengthening its sustainability, and minimizing environmental impacts. These new technologies hold promise towards the improvement of agriculture's few remaining unmechanized and drudging tasks. This paper reviews the robotics-automation and sensor-based approaches in the detection of weeds and their control strategies.
Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control
2016 ASABE International Meeting
Weed management is vitally important in crop production systems. However, conventional herbicide based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weed control offers a possibility of controlling weeds precisely, particularly for weeds growing near or within crop rows. A computer vision system was developed based on Kinect V2 sensor, using the fusion of two-dimensional textural data and three-dimensional spatial data to recognize and localized crop plants different growth stages. Images were acquired of different plant species such as broccoli, lettuce and corn at different growth stages. A database system was developed to organize these images. Several feature extraction algorithms were developed which addressed the problems of canopy occlusion and damaged leaves. With our proposed algorithms, different features were extracted and used to train plant and background classifiers. Finally, the efficiency and accuracy of the proposed classification methods were demonstrated and validated by experiments.
Innovation in mechanical weed control in crop rows
Weed Research, 2008
Weed control within crop rows is one of the main problems in organic farming. For centuries, different weed removal tools have been used to reduce weeds in the crop rows. Stimulated by the demand from organic farmers, research in several European countries over the last decade has focused on mechanisation using harrowing, torsion finger weeding and weeding with compressed air (Pneumat). Intelligent weeders are now being developed which offer more advanced ways to control weeds, including larger ones and to leave the crop plants unharmed. One of the first commercially available intelligent weeders, the Sarl Radis from France, has a simple crop detection system based on light interception, which guides a hoe in and out of the crop row, around the crop plants. The inclusion of innovative technologies, including advanced sensing and robotics, in combination with new cropping systems, might lead to a breakthrough in physical weed control in row crops leading to significant reductions, or even elimination, of the need for hand weeding.
Agrigras: Precision Farming for Weed Detection & Control
2020
This analysis has been supported by the employment of preciseness agriculture tools for the management of weeds in crops. It has focused on the creation of an image processing formula to sight the existence of weeds in an exceedingly specific website of crops. The most important objective has been to get formula so a weed detection system will be developed through binary classifications. The initial step of the image process is the detection of inexperienced plants to eliminate all the soil within the image, reducing data that are not necessary. Then, it's targeted on the vegetation by segmentation and eliminating unwanted data through medium and morphological filters. Finally, labeling objects have been created in the image so weed detection may be done employing a threshold based on the world of detection. This formula establishes correct observance of weeds and may be enforced in automated systems for the obliteration of weeds in crops, either through the employment of machin...