Sensor-based mechanical weed control: Present state and prospects

Machleb, Jannis; Peteinatos, Gerassimos G.; Kollenda, Benjamin; Andújar, Dionisio; Gerhards, Roland

doi:10.1016/j.compag.2020.105638

Cited by 76 publications

(61 citation statements)

References 89 publications

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“…Therefore, the reduction of herbicides, insecticides, and fungicides is a major motivating force behind current agricultural expert systems [7,8] to comply with EU Directive 2009/128/EC [9]. On that front, precision farming or smart farming has improved the potentials of automation in agricultural applications [10][11][12]. Tyagi [11] has especially made great progress in the automation of agriculture applications.…”

Section: Introductionmentioning

confidence: 99%

Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

et al. 2020

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The increasing public concern about food security and the stricter rules applied worldwide concerning herbicide use in the agri-food chain, reduce consumer acceptance of chemical plant protection. Site-Specific Weed Management can be achieved by applying a treatment only on the weed patches. Crop plants and weeds identification is a necessary component for various aspects of precision farming in order to perform on the spot herbicide spraying or robotic weeding and precision mechanical weed control. During the last years, a lot of different methods have been proposed, yet more improvements need to be made on this problem, concerning speed, robustness, and accuracy of the algorithms and the recognition systems. Digital cameras and Artificial Neural Networks (ANNs) have been rapidly developed in the past few years, providing new methods and tools also in agriculture and weed management. In the current work, images gathered by an RGB camera of Zea mays, Helianthus annuus, Solanum tuberosum, Alopecurus myosuroides, Amaranthus retroflexus, Avena fatua, Chenopodium album, Lamium purpureum, Matricaria chamomila, Setaria spp., Solanum nigrum and Stellaria media were provided to train Convolutional Neural Networks (CNNs). Three different CNNs, namely VGG16, ResNet–50, and Xception, were adapted and trained on a pool of 93,000 images. The training images consisted of images with plant material with only one species per image. A Top-1 accuracy between 77% and 98% was obtained in plant detection and weed species discrimination, on the testing of the images.

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Section: Introductionmentioning

confidence: 99%

Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

et al. 2020

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“…In the current scenario, food industries are considering the benefits of AI-based systems. Under the AI-based system, computer vision and deep-learning algorithm is very important and used to investigate the sequence of information or data received by the AI-based agents to trace the progress of crop and soil health [10,[12][13][14][15][16][17][18][19][20][21][22]. e computerized systems are used to make available clients with a sagacity of their soil's strengths and weaknesses.…”

Section: Soil Monitoringmentioning

confidence: 99%

“…In the literature, so many studies [13][14][15][16][17][18][19][20] were found on smart farming and noticed that by using these modern tools, resources can be utilized in an optimized manner and the income can be maximized. Also, there are some specific…”

Section: Soil Monitoringmentioning

confidence: 99%

“…e captured image is processed by a high-performing workstation to attend the maximum attentiveness of green band pixels relating to the crop line. Due to the outsized region captured by the input devices and the numerous processing line, an outstanding typical crop centre-line tracking is accomplished [19]. It evaluates the resultant image to a ground truth gridiron pattern with the crop line area.…”

Section: Robocropmentioning

confidence: 99%

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Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Kumar

Rawat

Mohd

et al. 2021

Journal of Food Quality

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The food processing and handling industry is the most significant business among the various manufacturing industries in the entire world that subsidize the highest employability. The human workforce plays an essential role in the smooth execution of the production and packaging of food products. Due to the involvement of humans, the food industries are failing to maintain the demand-supply chain and also lacking in food safety. To overcome these issues of food industries, industrial automation is the best possible solution. Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. This article is going to explain the AI applications in the food industry which recommends a huge amount of capital saving with maximizing resource utilization by reducing human error. Artificial intelligence with data science can improve the quality of restaurants, cafes, online delivery food chains, hotels, and food outlets by increasing production utilizing different fitting algorithms for sales prediction. AI could significantly improve packaging, increasing shelf life, a combination of the menu by using AI algorithms, and food safety by making a more transparent supply chain management system. With the help of AI and ML, the future of food industries is completely based on smart farming, robotic farming, and drones.

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“…Research to date has shown the potential of herbicide spot‐spraying (Esau et al, 2018) and of variable rate application (Kempenaar et al, 2017) for reducing the quantities of herbicides required. There are also some indications that mechanical weed control may likewise benefit from the use of intelligent control techniques (Lati et al, 2015; Machleb et al, 2020). Furthermore, new phenotyping approaches can be applied to elucidate the interactions between weeds and their environment and to facilitate decision‐making regarding the timing and choice of strategies for weed control (Comont and Neve, 2020).…”

Section: Introductionmentioning

confidence: 99%

Site‐specific weed management—constraints and opportunities for the weed research community: Insights from a workshop

et al. 2021

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The adoption of site-specific weed management (SSWM) technologies by farmers is not aligned with the scientific achievements in this field. While scientists have demonstrated significant success in real-time weed identification, phenotyping and accurate weed mapping by using various sensors and platforms, the integration by farmers of SSWM and weed phenotyping tools into weed management protocols is limited. This gap was therefore a central topic of discussion at the most recent workshop of the SSWM Working Group arranged by the European Weed Research Society (EWRS). This insight paper aims to summarise the presentations and discussions of some of the workshop panels and to highlight different aspects of weed identification and spray application that were thought to hinder SSWM adoption. It also aims to share views and thoughts regarding steps that can be taken to facilitate future implementation of SSWM.

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Sensor-based mechanical weed control: Present state and prospects

Cited by 76 publications

References 89 publications

Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Site‐specific weed management—constraints and opportunities for the weed research community: Insights from a workshop

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