No abstract

Food is the major source for the existence of the mankind. In order to meet the food requirements for the ever increasing population, the quantity of the food production has to be increased maintaining the quality standards. Agriculture is one such major sector which provides the food for mankind. It not only provides food but also supplies raw materials for industries. Implementation of advanced technologies such as Internet of Things in agriculture helps in improving the production with limited resources. The key parameter for the sustainable agriculture is moisture content available for the crop from the soil. This can be supplied efficiently by controlling the irrigation process. In this system an intelligent agriculture monitoring system with multiple wireless monitoring sensor nodes are used at different locations to monitor the parameters such as temperature, moisture content in the soil, humidity and rainfall. The data from the various sensors is aggregated at each node and transmitted to the coordinator station using long range transceiver. The data received at the coordinator station is subjected to a rule based decision making process to efficiently control the irrigation process. Also motor protection against the dry run was implemented, which not only protects the motor from break down but also avoids the unwanted power consumption. All the live data is in turn uploaded to the cloud so that the user can have a track of the current status of his farm at any time.

Fruits are one of the vital sources of nutrients for the mankind and their life span is very less. The fruit spoilage may occur at various stages such as, at the harvest time, during transportation, during storage etc. Freshness is a parameter used for accessing the quality of the fruit. About 20% of the harvested fruits are spoiled due to many factors, before consumption by humans. The spoilage of one fruit has a direct impact on the neighboring fruits. It is also a one of the indicators that gives an estimation of number of days that a fruit can be preserved. Early identification of the spoilage helps in taking the appropriate measures for the removal of spoiled fruits from the whole lot. So that it helps in preventing the spread of spoilage to its adjacent fruits. Deep learning based technological advancements helps in automatically identifying the spoiled fruits. In this work, internal quality attributes of the fruit are not taken into consideration for spoilage detection, only the external attributes are considered. The supervised learning technique is employed for the freshness analysis of two different types of fruits, Apple and Banana. As the 2 varieties are involved, it is a multiclass classification model with 4 classes. One shot detection technique is employed to accurately classify among the good fruit and spoiled fruit. Few images in the dataset are obtained from the kaggle.com and the rest are self - captured images. The dataset is balanced to avoid the biasness in the model. The model is implemented using Yolov4 and tiny Yolov4 frame works. These are one shot detection techniques, can be used for real time deployment. The inferences were obtained on the real time images and video. Confusion matrix is tabulated the performance metrics such as accuracy, F1 Score and recall are discussed with respect to these two techniques.