Robotic agricultural instrument for automated extraction of nematode cysts and eggs from soil to improve integrated pest management

Legner, Christopher; Tylka, Gregory L.; Pandey, Santosh

doi:10.1038/s41598-021-82261-w

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…Finally, we will use regression techniques to predict the probability of conception based on the collected data. Overall, the data analysis process is critical for gaining insights into the behavior of the sows and predicting the optimal time for conception [14]. The use of machine learning algorithms will help us process and analyze the large volumes of data generated by the sensors, and provide accurate and reliable results.…”

Section: B Data Analysismentioning

confidence: 99%

Automated Conception Detection in Sows Using RFID Technology

Ilangovan,

Natarajan,

Shobana

et al. 2023

Recent Trends in Data Science and Its Applications

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This paper presents an automated conception detection system using UHF RFID technology for monitoring the movement and activity of sows inside a pig pen. The system utilizes temperature, feeding pattern, and hormone level sensors to detect and predict the onset of estrus and pregnancy in sows. UHF RFID technology is used to track the movement of sows inside the pig pen, and the data is collected and analyzed by a microcontroller-based system. The system is designed to be cost-effective and easy to install, making it suitable for small pig farms. The current systems for monitoring the activity of pigs in Denmark and the US are very expensive and not feasible for small pig farms. Therefore, we propose an automated system that uses UHF RFID technology to monitor the movement of sows inside the pig pen. The system consists of a microcontroller-based unit, UHF RFID readers, and sensors for monitoring temperature, feeding patterns, and hormone levels. Temperature sensors are used to monitor the body temperature of sows, which changes during estrus and pregnancy. Feeding pattern sensors are used to monitor the feeding behavior of sows, which can provide valuable information about their reproductive status. Hormone level sensors are used to detect and measure hormone levels in the blood, which can indicate whether a sow is in estrus or pregnant. UHF RFID technology is used to track the movement of sows inside the pig pen. UHF RFID tags are attached to the sows, and UHF RFID readers are installed in various locations inside the pig pen. The readers detect the tags as the sows move around the pen, and the data is collected and analyzed by the microcontroller-based unit. The proposed system offers a cost-effective and easy-to-install solution for monitoring the activity of sows in small pig farms. The use of UHF RFID technology allows for accurate and efficient tracking of sow movement, and the integration of temperature, feeding pattern, and hormone level sensors provides valuable information about the reproductive status of the sows. Overall, this system has the potential to improve the reproductive performance of sows and increase the profitability of small pig farms.

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Section: B Data Analysismentioning

confidence: 99%

Automated Conception Detection in Sows Using RFID Technology

Ilangovan,

Natarajan,

Shobana

et al. 2023

Recent Trends in Data Science and Its Applications

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“…These algorithms based on convolutional networks were shown to be comparable to human evaluation in most situations [ 88 ]. The soil extraction and grinding of cysts (to release the eggs) were also automated in a robotic instrument that reproduced each step of a manual wet-sieving extraction [ 89 ]. Egg viability assessment can also be automated, for example, using a Complex Object Parametric Analyzer and Sorter (COPAS) system [ 90 ].…”

Section: Novel Resistance Strategies and Breeding Approachesmentioning

confidence: 99%

A Broad Review of Soybean Research on the Ongoing Race to Overcome Soybean Cyst Nematode

Nissan

Mimee

Cober

et al. 2022

Biology

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Plant pathogens greatly impact food security of the ever-growing human population. Breeding resistant crops is one of the most sustainable strategies to overcome the negative effects of these biotic stressors. In order to efficiently breed for resistant plants, the specific plant–pathogen interactions should be understood. Soybean is a short-day legume that is a staple in human food and animal feed due to its high nutritional content. Soybean cyst nematode (SCN) is a major soybean stressor infecting soybean worldwide including in China, Brazil, Argentina, USA and Canada. There are many Quantitative Trait Loci (QTLs) conferring resistance to SCN that have been identified; however, only two are widely used: rhg1 and Rhg4. Overuse of cultivars containing these QTLs/genes can lead to SCN resistance breakdown, necessitating the use of additional strategies. In this manuscript, a literature review is conducted on research related to soybean resistance to SCN. The main goal is to provide a current understanding of the mechanisms of SCN resistance and list the areas of research that could be further explored.

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“…This is laborious and requires the expertise of trained professionals to carefully handle the samples for cyst extraction. However, a recent work by Legner and colleagues [ 12 ] automate this process of extraction and soil analysis by using a “robotic agricultural instrument” equipped with elutriators and robotic handlers. This instrument takes 4 min to process 100 cc of soil as compared to the typical extraction style which takes about 10 min for extraction.…”

Section: Introductionmentioning

confidence: 99%

Soybean cyst nematode detection and management: a review

et al. 2022

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Soybeans play a key role in global food security. U.S. soybean yields, which comprise $$32\%$$ 32 % of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods—adopted by most of the laboratories to make recommendations—is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN’s increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.

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Robotic agricultural instrument for automated extraction of nematode cysts and eggs from soil to improve integrated pest management

Cited by 11 publications

References 32 publications

Automated Conception Detection in Sows Using RFID Technology

Automated Conception Detection in Sows Using RFID Technology

A Broad Review of Soybean Research on the Ongoing Race to Overcome Soybean Cyst Nematode

Soybean cyst nematode detection and management: a review

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