Technology for Kisan Samanvayam: Nutrition Intelligibility of Groundnut Plant using IoT-ML Framework

Neolithic Demographic transition resulting the reduction of habitable land for cultivation. Hence the smart agriculture is the only way to cater higher food demand. The farming community of developing countries like India needs Kisan Samanvayam with futuristic technologies for financially viable cultivation. Technology place vital role in economically nourishment of soil fertility and crop management. In this regard we proposed IoT-ML framework for remotely assessing the soil nutrients (N, P,K), PH and early stage detection of crop deceases. Android APP which is a part and parcel of the frame work enable the farmer to have real time visual statistics of the soil nutrients, notifications and suggestions regarding to the crop management. JXCT Soil NPK sensors, PH sensors, Dual Core ESP32 Controllers, Firebase Cloud and Random Forest Decision Tree machine Learning Algorithm, Micromlgen serve this purpose. Unlike Solitary sensor for entire field, we have divided a hector into four subregions for effective monitoring local region needs. The presence of IoT with TinyML increased the robustness of the framework and results are encouraging with sandy loam soil

Nurturing Agribusiness: A Sustainable Farming System for Tomato Crop Management Leveraging Machine Learning

The agriculture industry is undergoing a transformative shift with the introduction of IoT technology, enabling global connectivity for farmers. This technology offers a plethora of advantages, ranging from precise seed selection based on soil analysis to efficient crop maintenance, water management, and enhanced marketing support for improved profitability. To further enhance tomato farming practices, we propose the implementation of a smart farmer marketing assistant that streamlines the process of segregating yield based on its growth stage, reducing labor and time requirements.Further, the frame work is capable of early-disease management system that can detect  diseases like early blight,light blight, buck eye rot and anthranose and suggest remedy.  By adopting this innovative approach, financial losses associated with traditional methods are minimized.The traditional practice of combining all categories of vegetables (ripened, unripened, and partially rotten) in a single container often results in reduced shelf life for the produce. In our framework, we employ color sorting to categorize the vegetables, ensuring proper packing into their respective bins. This valuable data is made accessible through a cloud environment, providing potential buyers with comprehensive information about the yield, its category, and pricing. This increased visibility empowers farmers to reach a global market and sell their produce at competitive prices. In this context, we present a case study focused on the tomato crop, where we have successfully developed a prototype utilizing ESP32, a color sensor, and Google Firebase. This comprehensive framework effectively harnesses the power of IoT, Machine Learning, and potential marketing strategies, transforming the way farmers manage their crops and connect with buyers on a global scale with highly accurate 87.9% experimental results

Weighted Local Active Pixel Pattern (WLAPP) for Face Recognition in Parallel Computation Environment

Abstract  - The availability of multi-core technology resulted totally new computational era. Researchers are keen to explore available potential in state of art-machines for breaking the bearer imposed by serial computation. Face Recognition is one of the challenging applications on so ever computational environment. The main difficulty of traditional Face Recognition algorithms is lack of the scalability. In this paper Weighted Local Active Pixel Pattern (WLAPP), a new scalable Face Recognition Algorithm suitable for parallel environment is proposed.  Local Active Pixel Pattern (LAPP) is found to be simple and computational inexpensive compare to Local Binary Patterns (LBP). WLAPP is developed based on concept of LAPP. The experimentation is performed on FG-Net Aging Database with deliberately introduced 20% distortion and the results are encouraging. Keywords — Active pixels, Face Recognition, Local Binary Pattern (LBP), Local Active Pixel Pattern (LAPP), Pattern computing, parallel workers, template, weight computation

IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach

Food adulteration refers to the practice of deliberately adding substances to food to increase its volume, weight, or to improve its appearance, texture, or flavor; it is a significant issue that affects the health and safety of consumers. With the increasing demand for food, the risk of contamination and the intentional addition of harmful substances has increased. There are several existing methods for detecting food adulteration, including chemical analysis, microscopy, sensory analysis, etc. While these methods are helpful, they can be time-consuming, labor-intensive, and may not provide Real-time results. Using the Internet of Things (IoT), Machine Learning (ML) can significantly enhance the ability to identify food adulteration.Within this Framework, we are propose a solution to detect food adulteration using IoT and machine learning. The system comprises IoT sensors and devices to gather data on various parameters such as color, pH, gas content, etc. The collected data is fed into machine learning algorithms for preprocessing, analysis, and testing. Any anomalies or deviations from the standard patterns are flagged for further investigation. ML algorithms can continuously learn from the collected data, enabling them to enhance their accuracy and effectiveness over time. By implementing this system, we aim to create a Real-time, data- driven approach to detecting food adulteration, ensuring food safety and quality for consumers by creating a warning system

Automated Voice-to-Image Generation Using Generative Adversarial Networks in Machine Learning

Creating visuals from words may appear to be a complex process, but it is achievable with today’s technological advancements in Information Systems. Naturally, all the human-centric actions and assumptions may lead to visualization using Artificial Intelligence. In today’s Information Systems technological world, any item or a thing can be best described in pictorial form as a human person. Our paper aims to focus on providing machines with this intelligence. To complete this challenge, we used Natural Language Processing with Deep Learning. Our primary focus is on Generative Adversarial Networks. GANs will generate data based on word labels that are provided. NLP is also important since it helps to translate the provided speech into embedding vectors that the model can use. Our study is on the CUB dataset, which comprises bird photos. In today’s world, there are text-to-image generating models accessible. The authors investigated all of them, extending text-to-image generation to voice-to-image generation