Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, time-consuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and −0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

How communication affects the adoption of technologies in soybean production : a comparative study between Brazil and the United States

The digitalization of agriculture is one of the areas widely given importance in recent years as it could be the next agricultural revolution. This transformation is crucial in agribusiness because it leads to more informed decisions, higher efficiency, and easier knowledge sharing. In addition to increasing economic efficiency, more extensive use of precision technologies will be important in advancing societal goals relating to environmental impacts and climate change mitigation. However, studies have shown that the lack of ability to use these tools and the shortage of knowledge about the most appropriate technologies contribute to current farmer unease regarding digital technology. Information plays a relevant role in the technology adoption process in agriculture. This study investigates the influence of communication channels (mass media, social media, and interpersonal meetings) on farmers' adoption, decision-making, and benefits obtained concerning the use of precision and digital technologies. The study uses data from 461 soybean farmers in Brazil and 340 soybean farmers in the United States, the two largest producers and exporters of soybeans worldwide. The comparative study was conducted in Brazil's top five soybean-producing states and the United States' top nine soybean-producing states. These states provide approximately 75% of soybean production in each country. The strength of association between the communication channels and the level of adoption of technologies showed variations and similarities between Brazil and the United States. LinkedIn had the highest positive correlation in Brazil, with a strong relationship for seven precision and digital technologies among eight analyzed. In the United States, YouTube had the highest positive correlation with four of eight precision and digital technologies analyzed. The overall influence attributed to social media among Brazilian farmers was much higher than among American farmers. The relationship between communication channels and the perceived benefits of using technologies on-farm showed a higher association with mass media channels in the United States than in Brazil. Regarding making decisions and communication channels, the study showed a relevance of interpersonal meetings in Brazil and the United States. The results reinforce that superior knowledge and information are decisive in the process of adopting technologies in agriculture. Findings in the two countries enable farmers and agribusiness managers to use communication channels more effectively in evaluating and adopting precision technologies.A digitalização da agricultura se tornou uma das áreas de maior importância nos últimos anos, com potencial de se tornar a próxima revolução agrícola. Essa transformação é muito importante no agronegócio porque leva a decisões mais conscientes, aumenta a eficiência produtiva e facilita o compartilhamento de conhecimento. O uso mais extensivo de tecnologias de precisão é importante também para o avanço das metas sociais relacionadas aos impactos ambientais e à mitigação das mudanças climáticas. No entanto, muitos estudos empíricos e científicos têm mostrado que a falta de habilidade para usar essas ferramentas e a escassez de conhecimento sobre as tecnologias mais adequadas contribuem para o desconforto atual do agricultor em relação às tecnologias digitais. E nesse processo de adoção e implementação de novas tecnologias na agricultura, a informação desempenha um papel fundamental. Essa pesquisa investiga a influência dos canais de comunicação (mídia de massa, mídias sociais e relações interpessoais) na adoção, tomada de decisão e benefícios obtidos pelos agricultores com o uso de tecnologias. O estudo coletou dados com 461 produtores de soja no Brasil e 340 produtores de soja nos Estados Unidos, países líderes na produção e exportação de soja. O estudo comparativo foi realizado nos cinco principais Estados brasileiros produtores e nos nove principais Estados americanos produtores. Esses Estados representam aproximadamente 75% da produção de soja em cada país. Os resultados que mediram a associação entre os canais de comunicação e a adoção de tecnologias mostraram variações e semelhanças entre os dois países. O LinkedIn apresentou a maior correlação positiva no Brasil com sete tecnologias de precisão e digital entre oito analisadas. Nos Estados Unidos, o YouTube teve a maior correlação positiva com quatro das oito tecnologias digitais e de precisão analisadas. A influência geral atribuída às mídias sociais entre os agricultores brasileiros foi muito maior do que entre os agricultores americanos. A relação entre os canais de comunicação e os benefícios percebidos com o uso de tecnologias nas fazendas teve maior associação com a mídia de massa nos Estados Unidos do que no Brasil. Em relação à associação entre tomada de decisões e canais de comunicação, o estudo mostrou relevância das relações interpessoais no Brasil e nos Estados Unidos. Os resultados reforçam que conhecimento e informação são fatores decisivos no processo de adoção de tecnologias na agricultura. As descobertas desta pesquisa permitirão que agricultores e gestores usem os canais de comunicação de forma mais eficaz na avaliação e adoção de tecnologias de precisão

Strengthening Food Security through Technologies

Securing food for 1.35 billion Indians and doubling the income of farmers by 2022, as stated by the government, are challenging tasks. India’s performance is below average in all three aspects of food security: availability, affordability, and quality and safety. It is an irony that the nation with largest cultivable land in the world (142 million ha) is facing food insecurity in spite of wide agro-ecoregions that enable cultivation of land even for three seasons in the large area. A large population (58%) depends on agriculture for its livelihood but the contribution of this sector to country's gross domestic product (GDP) has declined continuously since 1950 and was 15% in 2018. Although, the country has transformed itself from dependency on imports to selfsufficiency still the challenge is to remove the farm distress in the country. Current farmers’ field yields are lower by two to four folds than the achievable potential. In addition, the value realisation from the market is 30 to 35% only. This is because 59 % of the farmers in India do not get essential information from any agency. The major hurdles for achieving the goals set by the government are low investment in agricultural technologies, low adoption of key technologies by the farmers largely due to lack of knowledge/information, poor physical infrastructure, and involvement of large number of intermediaries in the value chains. Lack of awareness among farmers about good agricultural management practices is a key factor for stagnant productivity levels. The mind-set of all actors involved in agriculture needs to change so that they work collectively as a team instead of working independently in silos if the agrarian situation is to be transformed

BENEFITS REGARDING THE IMPLEMENTATION OF AGRICULTURE 4.0 IN THE CURRENT CONTEXT

Agriculture 4.0 is comprised of different already operational or developing technologies such as robotics, nanotechnology, synthetic protein, cellular agriculture, gene editing technology, artificial intelligence, blockchain, and machine learning, which may have pervasive effects on future agriculture and food systems and major transformative potential. This paper presents some considerations regarding the technologies used in agriculture 4.0, namely: cheaper and more accurate sensors and microprocessors, cloud and IoT improvement and the use of radio units for data transmission and analysis and processing of large volumes of data

Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world\u27s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system

Setting the record straight on precision agriculture adoption

There is a perception that adoption of precision agriculture (PA) has been slow. This study reviews the public data on farm level use of PA in crop production worldwide. It examines adoption estimates for PA from completed surveys that utilized random sampling procedures, as well as estimates of adoption using other survey methods, with an objective to document the national or regional level adoption patterns of PA using existing data. The analysis indicates that Global Navigation Satellite Systems (GNSS) guidance and associated automated technologies like sprayer boom control and planter row or section shutoffs have been adopted as fast as any major agricultural technology in history. The main reason for the perception that PA adoption is slow is because PA is often associated with variable rate technology (VRT)—just one of many PA technologies, one of the first adopted by many farmers, but that now rarely exceeds 20% of farms. This level of adoption suggests that farmers like the idea of VRT, but are not convinced of its value. VRT adoption estimates for niche groups of farmers may exceed 50%. The biggest gap in PA adoption is for medium and small farms in the developing world that do not use motorized mechanization

Drones: Innovative Technology for Use in Precision Pest Management.

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers

Ireland’s Rural Environment: Research Highlights from Johnstown Castle

ReportThis booklet gives a flavour of the current research in Teagasc Johnstown Castle Research Centre and introduces you to the staff involved. It covers the areas of Nutrient Efficiency, Gaseous emissions, Agricultural Ecology, Soils and Water quality

Advanced strategies for optimization of primary nutrients requirement in rice-A review

The Green Revolution led to India's food independence mostly through the inclusion of supply-driven technologies, such as the introduction of high-yielding cultivars, improved access to water, agrochemicals, and mechanization. The present and future needs target agricultural sustainability without endangering the ecosystem. In this regard, the adoption of precision agriculture is required to meet this expected objective. In developed nations, precision farming has already experienced tremendous growth. However, precision farming methods have taken a while for emerging nations in Asia to comprehend, create, and embrace. Moreover, precision farming is frequently misunderstood as a sophisticated technological intervention intended for extensive agricultural fields. However, it is essentially a science that involves using the "right input" in the "right quantity," at the "right time," and in the "right place," to improve input use efficiency. In the case of primary nutrients such as nitrogen, phosphorus, and potassium, so-called recommendations for nutrient management based on soil tests have improved food grain output which increased the nutrient use efficiency up to a certain extent. Moreover, the recommendations are made for a given agroclimatic region and crops irrespective of site-specific soil fertility, cultivars, and agronomic management levels resulting in excess or scanty use to crop needs. At this juncture, assessing the nutritional requirements of plants proves to be a superior method, as it takes into account the cumulative impact of nutrient availability from various sources on plant growth at any specific stage, making it a reliable indicator of nutrient accessibility. Rice, the most important food crop, is grown in diverse agroclimatic regions at different management levels. Hence, there is an urgent need to adopt a precision nutrient management strategy to optimize the yield output. The article offers an overview of several precision instruments available for managing nutrients at specific sites and aids in choosing the most appropriate one for each circumstance