PRANAB DUTTA^1,*, GOMATHY MUTHUKRISHNAN^2,*, SABARINATHAN KUTALINGAM GOPALASUBRAMAIAM², RAJAKUMAR DHARMARAJ², ANANTHI KARUPPAIAH³, KARTHIBA LOGANATHAN⁴, KALAISELVI PERIYASAMY⁵, M. ARUMUGAM PILLAI², GK UPAMANYA⁶, SARODEE BORUAH⁷, LIPA DEB¹, ARTI KUMARI¹, MADHUSMITA MAHANTA¹, PUNABATI HEISNAM⁸, AK MISHRA⁹

BIOCELL, Vol.46, No.8, pp. 1843-1859, 2022, DOI:10.32604/biocell.2022.019291

Abstract

Plant growth-promoting rhizobacteria (PGPR) are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to, regulate the microbial dynamics and their interactions with the plants. These bacteria viz., Agrobacterium, Arthobacter, Azospirillum, Bacillus, Burkholderia, Flavobacterium, Pseudomonas, Rhizobium, etc., play important role in plant growth promotion. In addition, such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial, fungal and viral pathogens. The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic, siderophore, and hydrolytic enzymes, competition for space and… More >

R. Madhumathi^1,*, T. Arumuganathan², R. Shruthi¹

Computer Systems Science and Engineering, Vol.43, No.2, pp. 455-469, 2022, DOI:10.32604/csse.2022.023792

Abstract Precision agriculture is a modern farming practice that involves the usage of Internet of Things (IoT) to provide an intelligent farm management system. One of the important aspects in agriculture is the analysis of soil nutrients and balancing these inputs are essential for proper crop growth. The crop productivity and the soil fertility can be improved with effective nutrient management and precise application of fertilizers. This can be done by identifying the deficient nutrients with the help of an IoT system. As traditional approach is time consuming, an IoT-enabled system is developed using the colorimetry principle which analyzes the amount… More >

Fariza Sabrina^1,*, Shaleeza Sohail², Farnaz Farid³, Sayka Jahan⁴, Farhad Ahamed⁵, Steven Gordon⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3777-3797, 2022, DOI:10.32604/cmc.2022.026363

Abstract With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for… More >

Mirza Adnan Baig^*, Ghulam Ali Mallah, Noor Ahmed Shaikh

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4201-4213, 2022, DOI:10.32604/cmc.2022.026225

Abstract Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers… More >

Khalil Ibrahim Mohammad Abuzanouneh¹, Fahd N. Al-Wesabi², Amani Abdulrahman Albraikan³, Mesfer Al Duhayyim⁴, M. Al-Shabi⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶, K. Muthulakshmi⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 109-124, 2022, DOI:10.32604/cmc.2022.022648

Abstract Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model… More >

Misbah Naz¹, Rubab Shabbir^2,17, Krishan K. Verma³, Anshu Rastogi⁴, Vishnu D. Rajput⁵, Talha Javed^2,6, Muhammad Ammar Raza⁷, Kainat Asif⁸, Muhammad Aamir Iqbal⁹, Muhammad Imran¹⁰, Mohammad Sohidul Islam¹¹, Khalid Rehman Hakeem^12,13,*, Mehmet Firat Baran¹⁴, Ayman EL Sabagh^15,16,*

Phyton-International Journal of Experimental Botany, Vol.91, No.5, pp. 915-927, 2022, DOI:10.32604/phyton.2022.018688

Abstract

Under changing climate, trace elements like selenium (Se) have emerged as vital constituent of agro-ecosystems enabling crop plants to off-set the adverse effects of suboptimal growth conditions. The available form of selenium is important for boosting its bioavailability to crop plants having varied agro-botanical traits and root architectural systems. As compared to selenite, the selenate has a weaker soil bonding, higher absorption in the soil solution which results in a comparatively absorption by plant roots. Various factors including dry climate, high pH, optimal ambient air temperature, less accumulation of water, and low concentration of organic matter in the soil tend… More >

Zhenghua Yi¹, Paramsothy Jeyakumar², Jiang Jiang¹, Xiaomian Zhang³, Chunlei Yue³, Haijun Sun^1,*

Phyton-International Journal of Experimental Botany, Vol.91, No.4, pp. 859-868, 2022, DOI:10.32604/phyton.2022.018396

Abstract We conducted a pot experiment to examine the feasibility of applying a reaction-finished solution of hydrochar (HRFS) to enhance rice production in a saline soil. With this purpose, HRFS was applied (0, 10, 20, 40, 60, 80 and 100 mL/pot) and rice yield and nitrogen (N) use efficiency (NUE) were determined. HRFS application significantly (P < 0.05) increased rice grain yield by 19.6%–30.0% compared to the control treatment (CKU, with N but without HRFS addition). Moreover, HRFS application promoted plant height and straw biomass of rice. Increases of rice yield were mainly achieved by increases in the number of panicles and grains… More >

Rahul Sharma¹, Amar Singh¹, Kavita², N. Z. Jhanjhi³, Mehedi Masud⁴, Emad Sami Jaha⁵, Sahil Verma^2,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2125-2140, 2022, DOI:10.32604/cmc.2022.020017

Abstract Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and… More >

K. Sathya^1,*, M. Rajalakshmi²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 33-47, 2022, DOI:10.32604/csse.2022.022206

Abstract In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. This research focuses on identifying the plant diseases and detecting them promptly through the advancements in the field of computer vision. The images obtained from in-field farms are typically with less visual information. However, there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images. We propose a novel Reconstructed Disease Aware–Convolutional Neural Network (RDA-CNN), inspired by recent CNN architectures, that integrates image super… More >

Yi Liang^1,2, Haichao Wang^3,*, Wei-Chiang Hong⁴

Energy Engineering, Vol.119, No.1, pp. 163-188, 2022, DOI:10.32604/EE.2022.017396

Abstract Photovoltaics (PV) has been combined with many other industries, such as agriculture. But there are many problems for the sustainability of PV agriculture. Timely and accurate sustainability evaluation of modern photovoltaic agriculture is of great significance for accelerating the sustainable development of modern photovoltaic agriculture. In order to improve the timeliness and accuracy of evaluation, this paper proposes an evaluation model based on interval type-2 Fuzzy AHP-TOPSIS and least squares support vector machine optimized by fireworks algorithm. Firstly, the criteria system of modern photovoltaic agriculture sustainability is constructed from three dimensions including technology sustainability, economic sustainability and social sustainability. Then,… More >