Machine learning for soil moisture assessment

Rani, Alka; Kumar, Nirmal Tej; Kumar, Jitendra; Kumar, Jitendra; Sinha, Nishant K.

doi:10.1016/b978-0-323-85214-2.00001-x

Cited by 28 publications

(13 citation statements)

References 64 publications

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“…These advancements have revolutionized the process of soil moisture monitoring and prediction, offering a viable alternative to indirect methods reliant on water balance. Moreover, given the various data sources available, the application of ML has emerged as a promising approach for soil moisture prediction [33]- [36]. Over the years, various studies have compared conventional methodologies with machine learning techniques, including linear regression, support vector machines, random forests, and adaptive neuro-fuzzy inference systems.…”

Section: B Soil Moisture Predictionmentioning

confidence: 99%

IoT-Driven Machine Learning for Precision Viticulture Optimization

Pero,

Bakshi,

Nappi

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Precision Agriculture (PA), also known as Smart Farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, Precision Viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher-resolution meteorological and soil data obtained through in situ sensing. The integration of Machine Learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. This data allows ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a realworld scenario involving a vineyard located in southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.

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Section: B Soil Moisture Predictionmentioning

confidence: 99%

IoT-Driven Machine Learning for Precision Viticulture Optimization

Pero,

Bakshi,

Nappi

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Machine learning has become a transformative tool in agriculture, providing several uses for identifying diseases, managing crops and implementing precision agriculture (Andrew et al, 2022). Machine learning algorithms have the potential to significantly transform disease identification in agriculture, enabling more efficient monitoring and management of diseases (Rani et al, 2022). These algorithms have the ability to automate the processing of extensive datasets, such as crop photos, sensor data and weather data (Durai and Shamili, 2022).…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Algorithms for Early Detection of Legume Crop Disease

Cho

2024

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Background: Legume crops are an essential component of global agriculture and are widely supplied for human consumption, livestock feed and soil improvement due to their vital nutritional nature. The economic and nutritional significance of legumes is threatened by a multitude of diseases that can cause substantial yield losses. Traditional methods for disease detection, relying on visual inspection, are often subjective and inefficient, leading to delayed intervention. Methods: This study investigates the utilization of machine learning algorithms for the early identification of diseases affecting legume crops. A comprehensive evaluation is conducted on machine learning algorithms, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) with respect to the domain of disease detection. Through a comparative analysis of their performance across different environmental conditions and phases of crop development, this study also explores their strengths and weaknesses. Result: The findings and comparative examination offered significant perspectives on the potential of machine learning algorithms in the realm of early legume crop disease detection. In addition to enhancing crop health and disease management, the research provides support for sustainable agricultural practices and possesses the capacity to augment environmental sustainability and food security through the application of machine learning techniques.

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Section: Figurementioning

confidence: 99%

Recent Advances of Biosensors for Detection of Multiple Antibiotics

Lu,

Chen,

Rao

et al. 2023

Biosensors

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The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.

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Machine learning for soil moisture assessment

Cited by 28 publications

References 64 publications

IoT-Driven Machine Learning for Precision Viticulture Optimization

IoT-Driven Machine Learning for Precision Viticulture Optimization

Machine Learning Algorithms for Early Detection of Legume Crop Disease

Recent Advances of Biosensors for Detection of Multiple Antibiotics

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