Two key ideas for wireless sensor network (WSN) optimization in precision farming serve as the guidelines for this study. According to its definition, a wireless sensor network is a collection of sensor nodes that link to one another and/or with a node outside of their coverage area via wireless communication. These nodes frequently have one or more sensors for evaluating internal or external elements as well as environmental characteristics. A CPU then controls the sensors, compiling, storing, processing, and, if requested, transmitting the data. In some systems, the sensor nodes' processing power is constrained to make them more energy-efficient. In this application, the sensor nodes transfer the collected data to a remote node with more powerful processing capabilities and greater storage in specified time intervals or condition-based events storage. Because of its ability to monitor processes at a small scale, nanotechnology is regarded as a leading technology for controlling agriculture. So, it paves the way for crucial advantages such as improving food quality and quantity, lowering the input needed for agricultural production, fully utilizing soil nutrients, etc. The availability of natural resources, detecting the right nutrients from the soil for crop-specific production, and crop cultivation are issues in these models. In order to boost crop yield by assessing the nutrients in the soil, various nano-sensors are used in this research study. The type of crop that can be employed for cultivation or irrigation depends on the accuracy of acquisition and detection. Real-time nanosensors are used to collect the soil constituents that are necessary for crop productivity. In comparison to state-of-the-art models, simulation results utilizing a deep learning detector based on input obtained from nanosensors, which demonstrate increased productivity

A Hybrid Data Acquisition Model for Precision Agriculture using IoT, Engineering Nanomaterials and Artificial Intelligence

Precision agriculture relies on the early detection and isolation of crop diseases, and this research details how the You Only Look Once, Version 8 (YOLOv8) algorithm was used for the PlantVillage dataset. This research looks at how Deep Learning (DL) and Computer Vision (CV) could streamline and improve the diagnostic process, a problem with conventional disease detection approaches. The YOLOv8 model is trained and evaluated using the PlantVillage dataset, which consists of high-resolution photos encompassing different classes of crops and diseases. It is found that YOLOv8 outperformed other popular Machine Learning (ML) models in identifying agricultural diseases with 95% accuracy, 90% precision, 95% recall, 92% F1 score, and 90% specificity. Parameter optimization, advanced network architectures, and integration of the Internet of Things (IoT) and drones for real-time disease monitoring are just some of the future research directions proposed in this study, along with discussions of the difficulties posed by data availability, computational complexity, and resource requirements. YOLOv8's successful application to the PlantVillage dataset demonstrates its potential to automate and improve crop disease diagnosis, leading to more effective and environmentally friendly farming methods.

Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection

Genetic enhancement of reproductive stage drought tolerance in RPHR-1005R and derivative rice hybrids through marker-assisted backcross breeding in rice (Oryza sativa L.).

An optimized IoT based framework for enhancing mushroom cultivation

IoT is the necessary revolution in every industry. Any task is made simple when it can be observed and managed remotely. Every technical advancement should be undertaken in agriculture because it is such an important subject. The demand for agriculture has dramatically expanded due to the expansion in global population, but sadly, farmers are unable to meet the unending demand. IoT will be a better option than expanding the scale of agriculture.By cutting down on waste, precision agriculture can increase the output of any crop. The proposed work accomplishes this by keeping an eye on environmental factors that influence crop growth, such as temperature, humidity, soil moisture, etc. It also assists farmers in selecting an idle crop that will work best for them based on the information gathered and the environmental circumstances. The danger of crop failure, low yield, excessive water consumption, excessive fertiliser and pesticide use, etc. can be greatly decreased with this model, making it more effective than previous methods.The information gathered by the network nodes placed across the field is transmitted to the cloud, where it is evaluated and displayed for farmers' use. Farmers may make accurate and effective decisions that will affect their crops with the use of displayed data.

Analysis of Hydroponic System Crop Yield Prediction and Crop IoT-based monitoring system for precision agriculture

Drought stress has become more important abiotic factor affecting rice yield under present changing climate conditions. IR58025B is a maintainer line of widely used WA CMS line IR58025A and widely used in commercial rice hybrids production in India. Most of the rice hybrids are released for the irrigated ecosystem consequently, these derived hybrids suffer drastic yield decline under moisture stress conditions. To facilitate the efficient use of complex polygenic traits in hybrid rice molecular breeding research, we undertook development of introgression lines in background of IR58025B with tolerance to moisture stress by introgression of major QTL qDTY3.1 controlling grain yield under moisture stress. The present study reports the development and evaluation of 230 introgression lines (BC2F2) lines in IR58025B background for improvement of grain yield under moisture stress condition. Among 230 ILs, 24 introgression lines were found positive for qDTY3.1 and negative for fertility restoration (rf4 & rf3).Promising BILs exhibited earliness of 7 days under moisture stress with grain yield advantage of 8.54g to 10.78g in comparison to the recurrent parent. This suggested that the efficacy of introgressed drought tolerant QTL qDTY3.1 in enhancing grain yield under moisture stress conditions. Further, breeding lines of IR58025B possessing qDTY3.1 could also serve as good donors for development of drought tolerant rice hybrids for unfavorable ecologies.

Marker assisted backcross breeding to develop the drought tolerant version of IR58025B, a popular maintainer line of hybrid rice

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Analysis of Hydroponic System Crop Yield Prediction and Crop IoT-based monitoring system for precision agriculture

Marker assisted backcross breeding to develop the drought tolerant version of IR58025B, a popular maintainer line of hybrid rice