Timestamp Feature Variation based Weather Prediction Using Multi-Perception Neural Classification for Successive Crop Recommendation in Big Data Analysis

The recent generation has a lot of information for analysing growth in future prediction. Especially India is an extensive agricultural resource for the world's expansive economic growth. But in extensive data analysis, a problem for the recommendation of the seasonal crop is tedious because of improper feature analysis due to varying periods in weather conditions. So time variation-based big data analysis is essential for research improvement. To resolve this problem, we propose a Timestamp feature variation-based weather prediction using multi-perception neural classification (TFV-MPNC) for successive crop recommendation in big data analysis. Initially, the pre-processing was carried out to prepare the redundant noise dataset for fast prediction. Initially, the Preprocessing ensures the Contemporary Forecasting rate (CFR) for predicting the previous deficiency rate. Based on that Time stamp feature analysis (TSFA). The Dense region harvest rate (DRHR) was evaluated, and features were decision using Fuzzy intensive decision Function (FIDF), selected the scaled features and trained with multi-perception neural classification (MPNN). The proposed system produces higher forecasting by prediction features as well supportive to the weather dependences related to higher classification rate in precision, and recall has the best classification result

Precision Agriculture using Internet of thing with Artificial intelligence: A Systematic Literature Review

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this revie

The development and current state of the agricultural sector of the national economy due to the more active access to the global food market

The chapter examines the main geographical patterns of change in land productivity over the past 100 years of the development of agriculture in the south of Ukraine. The chapter also considers historical and contemporary global trends in the formation of the grain market, and identifies the role and place of Ukraine in i

Disruptive Technologies in Agricultural Operations: A Systematic Review of AI-driven AgriTech Research

YesThe evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations

Desarrollo de un método de estimación de rendimiento de cultivos agrícolas utilizando imágenes satelitales ópticas en la provincia de Buenos Aires

Tesis (Magister en aplicaciones de información espacial)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2020.Maestría conjunta con el Instituto de Altos Estudios Espaciales "Mario Gulich"-CONAE.El siguiente trabajo surge de una demanda concreta de la Agencia de Recaudación de la provincia de Buenos Aires (ARBA) con el objetivo de mejorar la calidad de los datos generados en la estimación del rendimiento de cultivos agrícolas extensivos. El alcance territorial de ARBA incluye toda la provincia de Buenos Aires (307.571 km2) y en este trabajo de investigación el área de estudio se reduce al partido de Daireaux (3.820 km2) que tiene como principal actividad productiva la agrícola-ganadera. Se utilizaron datos de campo provenientes de monitores de rendimiento (incorporados en las máquinas cosechadoras) y se vincularon con secuencias multitemporales de tres índices de vegetación diferentes (NDVI, MSAVI, EVI) y un índice de humedad (NDMI), utilizando la serie de satélites ópticos Landsat.The work arises from a specific demand of the Agencia de Recaudación de la Buenos Aires (ARBA) of improvement on yield estimation methods for extensive agricultural crops. The territorial scope of ARBA includes the whole province of Buenos Aires (307.571 km2) but in this work, the area studied is limited to Daireaux Department (3.820 km2), whose main productive activity is agriculture and livestock. Field data from yield monitors (incorporated in harvesting machines) were used and linked to multi-temporal sequences from three vegetation indices (NDVI, MSAVI, EVI) and a humidity index (NDMI), using Landsat optical satellites.Fil: Alvarez Zanelli, Emiliano. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Alvarez Zanelli, Emiliano. Universidad Nacional de Córdoba. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina.Fil: Alvarez Zanelli, Emiliano. Comisión Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina