Krisna Rani Sarker¹, Tahsina Sharmin Hoque^1,*, Nusrat Jahan Mim¹, Md. Anwarul Abedin¹, Md. Anamul Hoque¹, Ahmed Gaber², Mohammed M. Althaqafi³, Mohammad Anwar Hossain^4,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1711-1733, 2025, DOI:10.32604/phyton.2025.062465 - 27 June 2025

Abstract Improving crop productivity and soil fertility through the balanced application of inorganic and organic nutrient sources is a sustainable approach in modern agriculture. Char land soils, widely distributed in riverine Bangladesh, are generally low in organic matter status and deficient in necessary nutrient elements for crop production. Addressing this challenge, the present study was conducted to investigate the effects of various organic nutrient sources with inorganic fertilizers on crop yields, nutrient uptake, and soil fertility in farm (L₁) and char land (L₂) of Brahmaputra River in Mymensingh, Bangladesh from 2022 (Y₁) to 2023 (Y₂). For each location,… More >

Sanghati Banerjee¹, Om Pal², Tauseef Ahmad³, Shruti Kanga⁴, Suraj Kumar Singh^1,*, Bhartendu Sajan¹

Revue Internationale de Géomatique, Vol.34, pp. 71-88, 2025, DOI:10.32604/rig.2025.057963 - 24 February 2025

Abstract Agriculture is a primary activity in many countries, with wheat being a major cereal crop in India. Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dynamics, pricing, and trade. This study focuses on estimating wheat acreage and yield in Barwala block, Hisar district, Haryana, for the 2019–2020 Rabi season using remote sensing techniques. Multi-temporal satellite data capturing phenological stages of wheat (Seedling to Ripening) were processed using supervised classification with a maximum likelihood classifier in ERDAS Imagine. Wheat crop acreage was determined by overlaying ground truth points on… More >

Abdul Ghafoor¹, Maria Latif², Shafaqat Ali^2,3,*, Muhammad Munir^4,*, Muhammad Naeem Sattar⁵, Mohammed Ali Alshehri⁶

Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2683-2705, 2024, DOI:10.32604/phyton.2024.055898 - 30 November 2024

Abstract Heavy metal pollution in agricultural soils is a significant challenge for global food production and human health with the increasing industrialization and urbanization. There is a concern about introducing innovative techniques that are eco-friendly, cost-effective, and have the potential to alleviate metals, enhance crop growth, and protect plants against various environmental threats. For this, nanotechnology is one of the promising solutions having various applications in almost every field of life. This review explores various nano-based strategies that use nanoparticles (NPs) to lessen the harmful effects that heavy metals have on plants. Incorporated literature including published… More >

Abida Aziz¹, Shafiqa Bano¹, Mubshar Hussain², Muhammad Farooq Azhar³, Ghulam Yasin³, Naila Hadayat⁴, Iqra Arooj⁵, Abeer Hashem⁶, Ajay Kumar⁷, Elsayed Fathi Abd_Allah⁸, Qamar uz Zaman^9,10,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1647-1665, 2024, DOI:10.32604/phyton.2024.050974 - 30 July 2024

Abstract Wheat (Triticum aestivum L.) exhibits a greater capacity for cadmium (Cd) absorption compared to other cereal crops, leading to elevated daily Cd intake, and posing a significant threat to public health. For the mitigation of Cd stress in sustainable and environmentally friendly way, a pot study was designed by using exogenous application of various biostimulants, i.e., Nigella sativa and Ocimum sanctum extracts: 0%, 10%, and 20% in combination with the chelating agent ethylenediaminetetraacetic acid (EDTA) using 0 and 5 mg kg under various levels of Cd stress (i.e., 0, 5, 10, and 15 mg kg soil). Results revealed… More > Graphic Abstract

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Anindita Das¹, Kanu Murmu², Biplab Mitra³, Pintoo Bandopadhyay², Ritesh Kundu⁴, Moupiya Roy⁵, Saleh Alfarraj⁶, Mohammad Javed Ansari⁷, Marian Brestic⁸, Akbar Hossain^9,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 763-776, 2024, DOI:10.32604/phyton.2024.049473 - 29 April 2024

Abstract Nutrient management plays a crucial role in the yield and quality of sweet corn. A field experiment was conducted in consecutive two kharif seasons in 2018 and 2019 to investigate the effect of various organic sources of nutrients in combination with inorganic sources on the yield and quality of sweet corn under new alluvial soils of West Bengal, India. Treatments were: T: Control (without fertilizers); T: 100% recommended dose (RDF) of chemical fertilizers (CF) (RDF CF); T: 100% recommended dose of N (RDN) through vermicompost (VC) (RDN VC); T: 50 RDN through CF + 50%… More >

T. Sivaranjani^1,*, S. P. Vimal²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 153-170, 2023, DOI:10.32604/csse.2023.036724 - 26 May 2023

Abstract Crop Yield Prediction (CYP) is critical to world food production. Food safety is a top priority for policymakers. They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business. Crop Yield (CY) is a complex variable influenced by multiple factors, including genotype, environment, and their interactions. CYP is a significant agrarian issue. However, CYP is the main task due to many composite factors, such as climatic conditions and soil characteristics. Machine Learning (ML) is a powerful tool for supporting CYP decisions, including decision support on which… More >

K. Vignesh^1,*, A. Askarunisa², A. M. Abirami³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1051-1067, 2023, DOI:10.32604/csse.2023.024475 - 15 June 2022

Abstract Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep… More >

S. Vinson Joshua¹, A. Selwin Mich Priyadharson¹, Raju Kannadasan², Arfat Ahmad Khan³, Worawat Lawanont^3,*, Faizan Ahmed Khan⁴, Ateeq Ur Rehman⁵, Muhammad Junaid Ali⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5663-5679, 2022, DOI:10.32604/cmc.2022.027178 - 21 April 2022

Abstract The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data… More >

Gurram Sunitha^1,*, M. N. Pushpalatha², A. Parkavi³, Prasanthi Boyapati⁴, Ranjan Walia⁵, Rachna Kohar⁶, Kashif Qureshi⁷

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 423-437, 2022, DOI:10.32604/iasc.2022.024757 - 15 April 2022

Abstract Crop yield is an extremely difficult trait identified using many factors like genotype, environment and their interaction. Accurate Crop Yield Prediction (CYP) necessitates the basic understanding of the functional relativity among yields and the collaborative factor. Disclosing such connection requires both wide-ranging datasets and an efficient model. The CYP is important to accomplish irrigation scheduling and assessing labor necessities for reaping and storing. Predicting yield using various kinds of irrigation is effective for optimizing resources, but CYP is a difficult process owing to the existence of distinct factors. Recently, Deep Learning (DL) approaches offer solutions… More >