Ching-Lung Fan^1,*, Yu-Jen Chung², Shan-Min Yen^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4627-4653, 2025, DOI:10.32604/cmc.2025.066856 - 30 July 2025

Abstract Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and… More >

Meryam Manzoor¹, Konstantin Korolev², Maryam³, Riaz Ahmad^4,*

Phyton-International Journal of Experimental Botany, Vol.94, No.5, pp. 1401-1418, 2025, DOI:10.32604/phyton.2025.063930 - 29 May 2025

Abstract The productivity of fruit crops is badly affected by abrupt changes in climatic conditions. It is a matter of concern for fruit tree researchers to feed the huge population within the available resources. The adverse effects of abiotic stresses are increasing due to fluctuations in climate change. Several abiotic stresses (salinity, drought, water logging, minerals deficiency, temperature extremities and heavy metals) are reducing the overall productivity of crops. Therefore, the application of different management approaches, i.e., phytohormones, nanoparticles, organic amendments, microbes and molecular aspects are effective for the mitigation of abiotic stresses in fruit crops.… More >

Vicky Anand¹, Priyadarshani Rajput¹, Tatiana Minkina¹, Saglara Mandzhieva¹, Santosh Kumar², Avnish Chauhan³, Vishnu D. Rajput^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.5, pp. 1339-1365, 2025, DOI:10.32604/phyton.2025.063927 - 29 May 2025

Abstract The digital revolution in agriculture has introduced data-driven decision-making, where artificial intelligence, especially machine learning (ML), helps analyze large and varied data sources to improve soil quality and crop growth indices. Thus, a thorough evaluation of scientific publications from 2007 to 2024 was conducted via the Scopus and Web of Science databases with the PRISMA guidelines to determine the realistic role of ML in soil health and crop improvement under the SDGs. In addition, the present review focused to identify and analyze the trends, challenges, and opportunities associated with the successful implementation of ML in… 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 >

Farooq Ali^1,*, Huma Qayyum¹, Kashif Saleem², Iftikhar Ahmad³, Muhammad Javed Iqbal⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2373-2388, 2025, DOI:10.32604/cmc.2025.060745 - 17 February 2025

Abstract Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging… More >

Ching-Lung Fan^1,*, Yu-Jen Chung²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1925-1945, 2025, DOI:10.32604/cmc.2025.059245 - 17 February 2025

Abstract This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle (UAV) imagery by integrating the Visible Atmospherically Resistant Index (VARI) with deep learning models. The primary challenge addressed is the detection of bananas interplanted with betel nuts, a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap. The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector (SSD), You Only Look Once version 3 (YOLOv3), and Faster Region-Based Convolutional Neural Network (Faster RCNN)—using Red, Green, Blue (RGB) and VARI images for banana detection. Results More >

Qi Lu^1,2, Longfei Jin², Cuiling Tong³, Feng Liu², Bei Huang², Dejian Zhang^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 661-679, 2024, DOI:10.32604/phyton.2024.049733 - 29 April 2024

Abstract A Plant Biostimulant is any substance or microorganism applied to plants to enhance nutrition efficiency, abiotic stress tolerance, and/or crop quality traits, regardless of its nutrient content. The application of Plant biostimulants (PBs) in production can reduce the application of traditional pesticides and chemical fertilizers and improve the quality and yield of crops, which is conducive to the sustainable development of agriculture. An in-depth understanding of the mechanism and effect of various PBs is very important for how to apply PBs reasonably and effectively in the practice of crop production. This paper summarizes the main More >

Medhat A. Tawfeek^1,2, Nacim Yanes^3,4, Leila Jamel^5,*, Ghadah Aldehim⁵, Mahmood A. Mahmood^1,6

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179 - 29 November 2023

Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations… More >

Saud Yonbawi¹, Sultan Alahmari², T. Satyanarayana murthy³, Ravuri Daniel⁴, E. Laxmi Lydia⁵, Mohamad Khairi Ishak⁶, Hend Khalid Alkahtani^7,*, Ayman Aljarbouh⁸, Samih M. Mostafa⁹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3847-3864, 2023, DOI:10.32604/csse.2023.036552 - 03 April 2023

Abstract Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer… More >

Sasa Jing¹, Yuan Tian¹, Heng Zhang², John T. Hancock³, Ying Zhu^2,*, Ping Li^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1203-1214, 2023, DOI:10.32604/phyton.2023.026305 - 06 January 2023

Abstract The crop seeds have been a staple food for humans, and seed yield is important for sustaining agriculture development and enhancing human adaptability to food risks. The phenomenon of pre-harvest sprouting (PHS), caused by seed dormancy deficiency, and the phenomenon of low seedling emergence caused by seed deep dormancy, will lead to a reduction in agricultural production. Therefore, it is particularly important to understand the regulation mechanisms of seed dormancy. There are many studies on the regulation of seed dormancy in rice, but there are few studies on the regulation of seed dormancy in other More >