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Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

Abstract: Specification effects in terms of how technology options are contrasted can introduce bias in impact assessment. In a companion paper we evaluated the on-farm impacts of zero tillage (ZT) wheat as a resource-conserving technology in the rice–wheat systems of India''s Haryana State and Pakistan''s Punjab province [Erenstein, O., Farooq, U., Malik, R.K., Sharif, M., 2008. On-farm impacts of zero tillage wheat in South Asia''s rice–wheat systems. Field Crop Res. 105, 240–252]. The underlying field work surveyed plots of full adopters (only ZT plots), partial adopters (ZT and conventional tillage [CT] plots on same farm) and non-adopters (only CT plots). The companion paper assumes that the comparison between the ZT plots of full plus partial adopters and CT plots of partial adopters is the least-biased assessment of ZT''s on-farm impact. Here we revisit this underlying assumption and draw on complementary farm survey findings to illustrate the extent of specification effects in the assessment of on-farm impacts of ZT wheat in the same study areas. The study thereby distinguishes between three contrasts between ZT and CT within the same dataset: (1) plain contrast (all plots of adopters and non-adopters); (2) adopter-only contrast (full and partial adopters only, as used in Erenstein et al. [Erenstein, O., Farooq, U., Malik, R.K., Sharif, M., 2008. On-farm impacts of zero tillage wheat in South Asia''s rice–wheat systems. Field Crop Res. 105, 240–252]); (3) pair-wise contrast (partial adopters only). Proceeding from type 1 to type 3 comparisons the number of observations decrease but this is compensated by an increasing ability to control for variation. The ability of type 3 comparisons to pick up relatively small but significant differences is an important consideration in farm survey impact assessment. However, there is also an increase in partial adoption bias and further follow-up studies are needed to understand the implications of this. The comparisons show that the ZT effects on savings for diesel, tractor time and cost for wheat cultivation are particularly robust. In Haryana ZT''s positive implications for yield and the other financial indicators were also statistically robust, but in Punjab these were sensitive to specification effects. Particularly sensitive to specification effects were the ZT effects on the productivity of applied water in both sites. [Copyright &y& Elsevier]

Technological advances can significantly transform agrarian rural areas by increasing productivity and efficiency while reducing labour intensive processes. For instance, the usage of Unmanned Aerial Vehicles (UAVs) can offer flexibility collecting real-time information of the crops enabling farmers to take timely decisions. However, little is known about the barriers to the adoption of such technologies by rural farmers in emerging economies like India. Building on an extensive literature review, focussed group discussions, and field visits, the barriers impacting the adoption are identified and classified into technical, social, behavioural, operational, economic, and implementation categories. The relevance of each barrier and its importance is evaluated using a hybrid multi-criteria framework built on the theory of Fuzzy Delphi and Fuzzy Analytical Hierarchy Process to identify the most crucial barriers to the adoption of UAVs to implement precision agriculture in rural India. The paper suggests new avenues for accelerating technology adoption in rural areas of emerging economies. [Display omitted] • Adoption of UAVs to implement precision agriculture (PA) is in a nascent stage. • Comprehensive list of barriers impeding the adopting of UAVs to implement PA is identified. • Relevance and relative hierarchy of the barriers in Indian context is evaluated. • Measures to overcome the identified barriers are discussed. [ABSTRACT FROM AUTHOR]

Fisheries and aquaculture have emerged as a promising food production sector at the global level as well as regional levels. Fish and fish products are produced in one region and then are traded across various parts of the world. The whole sector is dependent on chains of workers engaged at landing centres, breeding units, processing units, and the trade and export chains. When the pandemic and the subsequent lockdown hit the country, it affected the whole sector and caused a huge decline in the production and trade of fishing products. This article aims to have a glimpse into the aquaculture sector and evaluate the impact of the pandemic on this sector in India. This valuable information can be of great importance to government and aquatic food suppliers so that they can evolve their strategies to withstand ongoing and future pandemics. It also helps them in framing regional and sectorial adjustments to transform investment plans, match market demands, improve monitoring strategies and get prepared for future needs. • The purpose of this article is to have a glimpse into the aquaculture sector and evaluate the impact of the COVID-19 pandemic on this sector in India. • This valuable information can be of great importance to the government and aquatic food suppliers so that they can evolve their strategies to withstand ongoing and future pandemics. • It also helps them frame regional and sectorial adjustments to transform investment plans, match market demands, improve monitoring strategies and prepare for future needs. • Some of the major challenges the entire fishing community faced during the COVID-19 lockdown are (i)difficulty in hiring truck drivers for transport of cargo. • (ii) Delayed seaweed seed material transportation. • (iii) Total shutdown of shrimp processing unit, forced engagement of minimal labour in shrimp feed, medicine and probiotics industries. • and (iv) Negative impacts on the livelihood of local fishermen due to fishing ban and low demand. • The paper also summarizes the reformations in the aquaculture sector in India to future pandemics. • Outbreak of COVID-19 has stimulated digital transformation across many aquaculture supply chain in India to reduce the dependence on manpower. • Scientific farming practices, the inclusion of technology and decreasing the over-dependency on export (around 90%) can recover the sector and can help take it to a desired level. [ABSTRACT FROM AUTHOR]

The advent of Wireless Sensor Networks (WSNs) spurred a new direction of research in agricultural and farming domain. In recent times, WSNs are widely applied in various agricultural applications. In this paper, we review the potential WSN applications, and the specific issues and challenges associated with deploying WSNs for improved farming. To focus on the specific requirements, the devices, sensors and communication techniques associated with WSNs in agricultural applications are analyzed comprehensively. We present various case studies to thoroughly explore the existing solutions proposed in the literature in various categories according to their design and implementation related parameters. In this regard, the WSN deployments for various farming applications in the Indian as well as global scenario are surveyed. We highlight the prospects and problems of these solutions, while identifying the factors for improvement and future directions of work using the new age technologies. [ABSTRACT FROM AUTHOR]

Housing units of around 20 million need to be constructed in India by 2022. One key challenge for government and industry is high demand for sustainable and affordable housing. This paper introduces the results of an industry focus group meeting with Indian Concrete Institute (ICI) and industrial associates, regarding needs for housing construction, novel products design and service life increase. Results show that present design methods for buildings and structures in India need improvement, from a whole life energy use perspective at a material and system level, service life improvement and real monitoring of buildings and structures. More than 50% of respondents are not happy with the existing buildings design codes as they do not help on energy use minimisation. Materials inefficiency in design, disconnection with real operational use and lack of durability tend to increase with high construction speed. It is highlighted that many of the technologies are not proven in local environments which is inhibiting their use, including reusing of blended ashes from agro-industry waste. Almost 240 million tonnes of CO 2 eq are emitted per year by Indian agricultural industry, which justify their re-use. Precast components are highlighted as a suitable solution in modular housing construction. [ABSTRACT FROM AUTHOR]