Héctor Alejandro Reza-Solis¹, Ofelia Adriana Hernández-Rodríguez^1,*, Andrés Francisco Martínez-Rosales², Dámaris Leopoldina Ojeda-Barrios¹

Phyton-International Journal of Experimental Botany, Vol.92, No.10, pp. 2737-2759, 2023, DOI:10.32604/phyton.2023.029851

Abstract Microalgae are mostly photoautotrophic microscopic organisms. According to their cellular structure, they are classified into two types, eukaryotes, and prokaryotes, and they are distributed in all types of ecosystems, presenting unique qualities due to the fact that they synthesize high value-added molecules used in various productive and environmental activities, and because their biomass is used as raw material to obtain various products. Therefore, the objective of this review was to collect, organize, and collate current information on the use of microalgae in the development of biotechnology involving the areas of agriculture, health, food, bioremediation, and biofuels. The results show that… More >

Anas Bilal^1,*, Xiaowen Liu¹, Haixia Long^1,*, Muhammad Shafiq², Muhammad Waqar³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2401-2426, 2023, DOI:10.32604/cmc.2023.037857

Abstract Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease datasets is used to detect… More >

Sana Parez¹, Naqqash Dilshad², Turki M. Alanazi³, Jong Weon Lee^1,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… 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

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 crops to grow in a… 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

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 Learning based Insect Pest Classification… More >

Xuejun Zhang^1,#, Muhammad Amjad Bashir^2,#, Qurat-Ul-Ain Raza³, Xiaotong Liu¹, Jianhang Luo¹, Ying Zhao¹, Qiuliang Lei⁴, Hafiz Muhammad Ali Raza^2,3, Abdur Rehim^2,3, Yucong Geng⁴, Hongbin Liu^4,*

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1349-1360, 2023, DOI:10.32604/phyton.2023.026952

Abstract

The rapidly growing world population, water shortage, and food security are promising problems for sustainable agriculture. Farmers adopt higher irrigation and fertilizer applications to increase crop production resulting in environmental pollution. This study aimed to identify the long-term effects of intelligent water and fertilizers used in corn yield and soil nutrient status. A series of field experiments were conducted for six years with treatments as: farmer accustomed to fertilization used as control (CON), fertilizer decrement (KF), fertilizer decrement + water-saving irrigation (BMP1); combined application of organic and inorganic fertilizer + water-saving irrigation (BMP2), and combined application of controlled-release fertilizer (BMP3).… More >

Xiaotong Liu^1,#, Muhammad Amjad Bashir^2,3,#, Yucong Geng⁴, Qurat-Ul-Ain Raza², Abdur Rehim², Muhammad Aon², Jianhang Luo¹, Ying Zhao¹, Xuejun Zhang^1,*, Hongbin Liu^4,*

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1555-1570, 2023, DOI:10.32604/phyton.2023.026735

Abstract

The increasing world population has forced excessive chemical fertilizer and irrigation to complete the global food demand, deteriorating the water quality and nutrient losses. Short-term studies do not compile the evidences; therefore, the study aimed to identify the effectiveness of reduced doses of inorganic fertilizer and water-saving practices, hence, a six-year experiment (2015–2020) was conducted in China to address the knowledge gap. The experimental treatments were: farmer accustomed fertilization used as control (525:180:30 kg NPK ha⁻¹), fertilizer decrement (450:150:15 kg NPK ha⁻¹), fertilizer decrement + water-saving irrigation (450:150:15 kg NPK ha⁻¹), application of organic and inorganic fertilizer + water-saving irrigation… More >

Saud Yonbawi¹, Sultan Alahmari², B. R. S. S. Raju³, Chukka Hari Govinda Rao⁴, Mohamad Khairi Ishak⁵, Hend Khalid Alkahtani⁶, José Varela-Aldás^7,*, Samih M. Mostafa⁸

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2319-2335, 2023, DOI:10.32604/csse.2023.036721

Abstract Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a… More >

Saud Yonbawi¹, Sultan Alahmari², T. Satyanarayana Murthy³, Padmakar Maddala⁴, E. Laxmi Lydia⁵, Seifedine Kadry^6,7,8,*, Jungeun Kim⁹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1533-1547, 2023, DOI:10.32604/csse.2023.036296

Abstract Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the… More >

Muhammad Asad¹, Rashad Rasool Khan^2,*, Ahmed B. Aljuboory³, Muhammad Haroon U. Rashid⁴, Uttam Kumar⁵, Inzamam Ul Haq⁶, Aqsa Hafeez⁷, Ahmed Noureldeen⁸, Khadiga Alharbi^9,*

Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1161-1172, 2023, DOI:10.32604/phyton.2023.026513

Abstract The beet armyworm (BAW), Spodoptera exigua (Lepidoptera: Noctuidae) is a highly destructive pest of vegetables and field crops. Management of beet armyworm primarily relies on synthetic pesticides, which is threatening the beneficial community and environment. Most importantly, the BAW developed resistance to synthetic pesticides with making it difficult to manage. Therefore, alternative and environment-friendly pest management tactics are urgently required. The use of pesticidal plant extracts provides an effective way for a sustainable pest management program. To evaluate the use of pesticidal plant extracts against BAW, we selected six plant species (Lantana camara, Aloe vera, Azadirachta indica, Cymbopogon citratus, Nicotiana tabacum ,… More >