Abstract:Development and deployment of sensing technologies is one of the main steps in achieving sustainability in crop production through precision agriculture. Key sensing methodologies developed for monitoring soil moisture and nutrients with recent advances in the sensing devices reported in literature using those techniques are overviewed in this article. The soil moisture determination has been divided into four main sections describing soil moisture measurement metrics and laboratory-based testing, followed by … Show more
“…Soil characteristics are of a great importance in many applied fields, such as agriculture [1], construction [2], and earthing installation [3]. One of the important physical properties of soil is the electrical resistivity (ρ), which can be defined as the resistance in ohms between the opposite faces of a unit cube of the material.…”
Soil resistivity depends on many overlapping factors, which influence it in various ways. The aim of this study was to determine the effects of some physical and chemical factors on soil apparent resistivity. The results of field, laboratory, and statistical studies revealed a complex relationship between water content, pH, and salinity with soil apparent resistivity. The results showed that water content had a clear effect on apparent resistivity, as it increased significantly when water content value decreased to less than about 5%. The results also showed that increasing the salinity ratio at the expense of water content led to an increase in the apparent resistivity values. The apparent resistivity values also increased significantly when pH values fell below about 7.7.
The increase in air temperature caused an increase in water evaporation from the soil, which led to increasing the apparent resistivity. The rise in air temperature also caused an increase in the concentration of salts at the expense of water content; since salts are considered to be insulators, unless they are dissolved in water, they cause an increase in the value of apparent resistivity.
“…Soil characteristics are of a great importance in many applied fields, such as agriculture [1], construction [2], and earthing installation [3]. One of the important physical properties of soil is the electrical resistivity (ρ), which can be defined as the resistance in ohms between the opposite faces of a unit cube of the material.…”
Soil resistivity depends on many overlapping factors, which influence it in various ways. The aim of this study was to determine the effects of some physical and chemical factors on soil apparent resistivity. The results of field, laboratory, and statistical studies revealed a complex relationship between water content, pH, and salinity with soil apparent resistivity. The results showed that water content had a clear effect on apparent resistivity, as it increased significantly when water content value decreased to less than about 5%. The results also showed that increasing the salinity ratio at the expense of water content led to an increase in the apparent resistivity values. The apparent resistivity values also increased significantly when pH values fell below about 7.7.
The increase in air temperature caused an increase in water evaporation from the soil, which led to increasing the apparent resistivity. The rise in air temperature also caused an increase in the concentration of salts at the expense of water content; since salts are considered to be insulators, unless they are dissolved in water, they cause an increase in the value of apparent resistivity.
“…This article focuses primarily on the sensing methodologies developed in agriculture for monitoring diseases and stress caused due to biotic aggressors-pathogens and pests. A recent article by our group provided a comprehensive survey of soil moisture and nutrient sensing that cover the key abiotic sources of stress [19]. This section presents an overview of the responses of biotic stresses in plants that enables the development of technologies to detect them.…”
Section: How Do Plants Respond To Stress?mentioning
confidence: 99%
“…Methodologies enabling early and accurate detection of plant stress due to pathogens and pests provide a way for optimal deployment of countermeasures for reducing of losses in yield. Our group has been pursuing agriculture sensor design for soil [5][6][7][8][9][10][11][12][13][14][15][16][17] and plant health [18][19][20][21][22][23][24][25][26], modeling for soil moisture/nutrients and plant growth dynamics [27,28], and decision-making for irrigation and fertilization for over a decade [27,29].…”
Reducing agricultural losses is an effective way to sustainably increase agricultural output efficiency to meet our present and future needs for food, fiber, fodder, and fuel. Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and deploying efficient measures. This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops. Key sensing methodologies for plant pathogen (or phytopathogen), as well as herbivorous insects/pests are presented, where the working principles are described, and key recent works discussed. The detection methods overviewed for phytopathogen-related stress identification include nucleic acid-based methods, immunological methods, imaging-based techniques, spectroscopic methods, phytohormone biosensing methods, monitoring methods for plant volatiles, and active remote sensing technologies. Whereas the pest-related sensing techniques include machine-vision-based methods, pest acoustic-emission sensors, and volatile organic compound-based stress monitoring methods. Additionally, Comparisons have been made between different sensing techniques as well as recently reported works, where the strengths and limitations are identified. Finally, the prospective future directions for monitoring biotic stress in crops are discussed.
“…However, certain traits can be understood with the help of three phenomena: reflectance, absorption, and emittance, that occur naturally in all plants [ 6 ]. The current record on the advancements in agriculture instrumentation shows that all these phenomena can be captured with the help of imaging sensors [ 7 ]. It has been found that the best way to quantify water stress in plants is by measuring the reemittance of light from the plant surface.…”
Phenomics and chlorophyll fluorescence can help us to understand the various stresses a plant may undergo. In this research work, we observe the image-based morphological changes in the wheat canopy. These changes are monitored by capturing the maximum area of wheat canopy image that has maximum photosynthetic activity (chlorophyll fluorescence signals). The proposed algorithm presented here has three stages: (i) first, derivation of dynamic threshold value by curve fitting of data to eliminate the pixels of low-intensity value, (ii) second, extraction and segmentation of thresholded region by application of histogram-based
K
-means algorithm iteratively (this scheme of the algorithm is referred to as the curve fit
K
-means (CfitK-means) algorithm); and (iii) third, computation of 23 grey level cooccurrence matrix (GLCM) texture features (traits) from the wheat images has been done. These features help to do statistical analysis and infer agronomical insights. The analysis consists of correlation, factor, and agglomerative clustering to identify water stress indicators. A public repository of wheat canopy images was used that had normal and water stress response chlorophyll fluorescence images. The analysis of the feature dataset shows that all 23 features are proved fruitful in studying the changes in the shape and structure of wheat canopy due to water stress. The best segmentation algorithm was confirmed by doing exhaustive comparisons of seven segmentation algorithms. The comparisons showed that the best algorithm is CfitK-means as it has a maximum IoU score value of 95.75.
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