Abstract:Banana is one of the most consumed fruits in the world due to its flavor and nutritional value. The knowledge of the ripening stage of bananas is essential for its commercialization, since, after harvesting it can take days in transportation until reaching their destination. The use of spectroscopic techniques in the infrared region has been widely used in the food industry. In this study, a nondestructive analytical method was developed to monitor the maturation of silver bananas, dwarf bananas, gold bananas … Show more
“…These images are then analysed to determine the distribution and density of crops throughout the field. This method allows farmers to efficiently monitor crop health, identify areas of overgrowth or undergrowth, and make informed decisions regarding irrigation, fertilization, and pest management [15][16][17][18].…”
Aims: Smart farming and precision agriculture have emerged as transformative paradigms in modern agricultural practices, leveraging technological innovations to enhance productivity, sustainability, and efficiency in food production. This paper provides a comprehensive review of the concepts and technologies related to smart farming. The work proposed implements a sensor assisted IoT based model to enhance the overall yield of banana crop, by monitoring the current conditions existing at the site and predicting the alterations to achieve optimal conditions for conducive and healthy growth, further early identification and prediction of infection causing pathogens to have also been embedded in the model. Model designed has been tested on the farm field of banana crop located in the premises of Amity University Lucknow Campus equipped with moisture and humidity sensors (DHT11), NPK soil nutrient sensor, PIR motion sensor (HC-SR501), pH Sensor (pH -450), rain drop sensor along with camera installed drone for infection identification. Data from sensors and drone camera were monitored at regular intervals for future predictions about health and productivity estimate of the crop.
“…These images are then analysed to determine the distribution and density of crops throughout the field. This method allows farmers to efficiently monitor crop health, identify areas of overgrowth or undergrowth, and make informed decisions regarding irrigation, fertilization, and pest management [15][16][17][18].…”
Aims: Smart farming and precision agriculture have emerged as transformative paradigms in modern agricultural practices, leveraging technological innovations to enhance productivity, sustainability, and efficiency in food production. This paper provides a comprehensive review of the concepts and technologies related to smart farming. The work proposed implements a sensor assisted IoT based model to enhance the overall yield of banana crop, by monitoring the current conditions existing at the site and predicting the alterations to achieve optimal conditions for conducive and healthy growth, further early identification and prediction of infection causing pathogens to have also been embedded in the model. Model designed has been tested on the farm field of banana crop located in the premises of Amity University Lucknow Campus equipped with moisture and humidity sensors (DHT11), NPK soil nutrient sensor, PIR motion sensor (HC-SR501), pH Sensor (pH -450), rain drop sensor along with camera installed drone for infection identification. Data from sensors and drone camera were monitored at regular intervals for future predictions about health and productivity estimate of the crop.
Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.