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Plant Disease Detection Using Imaging Sensors, Deep Learning and Machine Learning for Smart Farming
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Chanchal Upadhyay, Hemant K Upadhyay, Sapna Juneja, Abhinav Juneja
On the farm, soil moisture sensors, temperature and humidity sensors are deployed to detect diseases on a plant. The data obtained from the sensor is sent to the computer via wired or wireless devices and is confirmed and checked with relevant figures such as moisture values in the server-side data. If there is any difference in price, information is conveyed to the farmer electronically. The outputs of the sensor are created electronically and the farmer receives the complete crop and environment data online. Crop disease is detected by the image. The camera system remains around the crop to get an image of the leaves. The images obtained are sent to the server and then back to the farmer using image processing techniques, web pages on the app and leaf positions on mobile phones (Figure 9.2).
Time-Series Analysis of COVID-19 in Iran: A Remote Sensing Perspective
Published in Abbas Rajabifard, Greg Foliente, Daniel Paez, COVID-19 Pandemic, Geospatial Information, and Community Resilience, 2021
Nadia Abbaszadeh Tehrani, Abolfazl Mollalo, Farinaz Farhanj, Nooshin Pahlevanzadeh, Milad Janalipour
Soil Moisture (SM) is an environmental indicator that can be provided by RS data. Change of SM may influence COVID-19 cases. Hence, this indicator is included in this study. SMAP measures surface values and subsurface SM (mm) every 3 days at 0.25 arc degrees (≈ 27.75 km) resolution with the combination of passive (radiometer) and active (radar) instruments [36]. Scientists can use SMAP data products to better investigate different environmental applications, such as drought monitoring, climate change analyzing, flood prediction, and monitoring of agricultural crop growth [37, 38, 39]. These data were evaluated and validated in some research projects [40, 41]. In this study, level 3 surface soil moisture (SSM) with SDS name of SSM was acquired.
Value-Added Products and Bioactive Compounds from Fruit Wastes
Published in Megh R. Goyal, Arijit Nath, Rasul Hafiz Ansar Suleria, Plant-Based Functional Foods and Phytochemicals, 2021
Ranjay Kumar Thakur, Rahel Suchintita Das, Prashant K. Biswas, Mukesh Singh
Banana (Musa acuminate and Musa balbisiana) peel are the major by-product, which amounts to 30% (nearly) of the fruit [60]. The ratio of banana waste and product is 2:1. The lignocellulosic mass is discarded or transported to open dumping grounds. The process helps to preserve the soil moisture and supply organic matter, but has a latent risk of transmission of diseases. The lignocellulosic biomass produces greenhouse gases during decomposition [18].
Doubly multivariate linear models with block exchangeable distributed errors and site-dependent covariates
Published in Journal of Applied Statistics, 2022
In this paper, the derived novel Rao's score test (RST) statistic for testing the intercept and slope parameters is applied to an agricultural dataset (Table 1, Ratkowsky and Martin, [17]) on apple fruit trees with two site-dependent covariates, namely mean fruit weight (MFW) and bitter pit (BP). An important physiological disorder of apple fruits is its BP. BP is not a disease, but just a disorder, which is caused by low levels of calcium in the fruit, and it is more common after hot and dry summers. It can usually be reduced, or sometimes, prevented with good cultivation practices. Excessive nitrogen or potassium seems to coincide with BP as does fluctuating soil moisture. BP, a postharvest disorder of apples, is often thought to be linked to fruit size, with larger fruit being more susceptible. Nonetheless, some researchers have shown that there is no relationship between fruit size and the calcium levels, so fruit size is not the only factor making the fruit prone to BP.
Drought-related cholera outbreaks in Africa and the implications for climate change: a narrative review
Published in Pathogens and Global Health, 2022
Gina E. C. Charnley, Ilan Kelman, Kris A. Murray
Declines in precipitation have been seen over parts of Africa, while increases are seen in others, a differentiation suggested as increasing with climate change. The Indian Ocean Dipole may play a role in these changes [53,54], reducing water availability and fundamentally leading to drought in some areas. Despite this, water availability is a complex phenomenon that must account for alterations in not just the source (precipitation, groundwater, soil moisture, evapotranspiration) but also agriculture, infrastructure, and human behavior. For example, in modeling studies a net increase in freshwater resources was seen for most African countries, whereas northern regions saw more extreme dryness and serious agricultural system issues over the Sahel, Horn of Africa, and southern Africa [55]. Eighty four percentage of the population in Africa do not have access to piped water into their yard or dwelling and water fetching is mainly carried out by women and children. This domestic burden decreases the time available for education and employment, potentially stunting development if water resources become scarcer. Poor water availability is also linked to poor hygiene behaviors such as reduced hand washing, potentially increasing cholera outbreaks in these areas [43].
Forecasting drought using neural network approaches with transformed time series data
Published in Journal of Applied Statistics, 2021
O. Ozan Evkaya, Fatma Sevinç Kurnaz
Generally, drought can be described as a deficiency in precipitation, soil moisture, and ground water over a certain time period. There is no universal definition for drought but mainly it can be classified as; meteorological, agricultural, hydrological, and socio-economic. The difference on these types is directly attached to the evolution time of drought, which makes it more difficult to monitor and manage. However, the development of early warning system with sustainable monitoring tools is very crucial for decision makers. In that respect, long term drought forecasts for a given region present valuable information and makes possible to mitigate its various consequences. With the help of data-driven models, which requires less information for forecasting purposes, a wide range of forecasting studies are conducted [1]. One important issue is to know the difference between the amount of water supply and demand for drought assessment. This situation yields to improve several drought indexes and therefore a great number of indicators and indices for monitoring drought have been proposed in literature [15].