Farmers’ Toolkit: Deep Learning in Weed Detection and Precision Crop & Fertilizer Recommendations

Agriculture is widely recognized as a significant and indispensable occupation on a global scale. The current imperative is to optimize agricultural practices and progressively transition towards smart agriculture. The Internet of Things (IoT) technology has dramatically enhanced people’s daily lives via diverse applications across several domains. Previous studies have yet to effectively incorporate Artificial Intelligence (AI) with sensor technology to provide comprehensive guidance to agricultural practitioners, hindering their ability to achieve good outcomes. This research offers Farmers’ Toolkit with four layers: sensor, network, service, and application. This toolkit aims to facilitate the implementation of a smart farming system while effectively managing energy resources. With a specific emphasis on the application layer, the toolkit uses a deep learning methodology to construct a fertilizer recommendation system that aligns with the expert’s perspective. This study utilizes IoT devices and Wireless Sensor Network (WSN) methods to enhance the efficiency and speed of recommending appropriate crops to farmers. The recommendation process considers several criteria: temperature, yearly precipitation, land area, prior crop history, and available resources. The identification of undesirable vegetation on agricultural fields, namely the detection of weeds, is carried out using drone technology equipped with frame-capturing capabilities and advanced deep-learning algorithms. The findings demonstrate an accuracy rate of 94%, precision rate of 92%, recall rate of 96%, and F1 score of 94%. The toolkit for farmers alleviates physical labor and time expended on various agricultural tasks while enhancing overall land productivity, mitigating potential crop failures in specific soil conditions, and minimizing crop damage inflicted by weeds

Applications of Sustainable Business Models for PV Systems in Developing Countries

The global push to boost the adoption of renewable energy resources and decrease our dependency on fossil fuels for electricity generation has experienced substantial growth. Solar Photovoltaic (PV) panels have now achieved a level of extensive implementation and global economic feasibility. These panels, compact and resilient, require only sunlight exposure to generate electricity. Since their commercial use began in Europe in the 1990s, solar PV power has been electrifying countless households worldwide and providing energy access to numerous remote communities in less developed regions. As a result, the extensive global deployment of solar energy systems strengthens the energy industry and fosters job growth, thereby facilitating substantial progress. This study emphasizes the importance of Photovoltaic (PV) technologies and their contribution to advancing sustainability, particularly in emerging economies. It provides valuable perspectives and examinations of the sustainability of solar energy, covering both ecological and economic facets.Furthermore, it delineates the crucial contribution of PV technologies to sustainable development, as they meet energy needs, create job prospects, and enhance environmental conservation initiatives

Drive State Analysis Based Electric Drive Control Model for Improved Power Stabilization

The problem of power stabilization in electric drives has been well studied. There exist numbers of approaches around the problem which consider the input power alone and suffer to achieve higher performance in power stabilization. To handle this issue, an efficient Drive State Analysis based Electric Drive Control model (DSA-EDCM) is presented in this article. The model monitors the drive state of electric drive at each duty cycle. According to the drive state and its previous conditions like voltage consumption, voltage leak, rpm and torque required, the method performs drive state analysis. The drive state analysis algorithm computes the power required at different conditions by computing Power Support value (PSV). Based on the PSV value, the method selects specific drive according to the input voltage received. Selected drive has been triggered for the cycle to maintain power stability. The proposed model improves the performance of power stability and maximizes the utilization performance

Farmers’ Toolkit: Deep Learning in Weed Detection and Precision Crop & Fertilizer Recommendations

Agriculture is widely recognized as a significant and indispensable occupation on a global scale. The current imperative is to optimize agricultural practices and progressively transition towards smart agriculture. The Internet of Things (IoT) technology has dramatically enhanced people’s daily lives via diverse applications across several domains. Previous studies have yet to effectively incorporate Artificial Intelligence (AI) with sensor technology to provide comprehensive guidance to agricultural practitioners, hindering their ability to achieve good outcomes. This research offers Farmers’ Toolkit with four layers: sensor, network, service, and application. This toolkit aims to facilitate the implementation of a smart farming system while effectively managing energy resources. With a specific emphasis on the application layer, the toolkit uses a deep learning methodology to construct a fertilizer recommendation system that aligns with the expert’s perspective. This study utilizes IoT devices and Wireless Sensor Network (WSN) methods to enhance the efficiency and speed of recommending appropriate crops to farmers. The recommendation process considers several criteria: temperature, yearly precipitation, land area, prior crop history, and available resources. The identification of undesirable vegetation on agricultural fields, namely the detection of weeds, is carried out using drone technology equipped with frame-capturing capabilities and advanced deep-learning algorithms. The findings demonstrate an accuracy rate of 94%, precision rate of 92%, recall rate of 96%, and F1 score of 94%. The toolkit for farmers alleviates physical labor and time expended on various agricultural tasks while enhancing overall land productivity, mitigating potential crop failures in specific soil conditions, and minimizing crop damage inflicted by weeds