Article
Agronomy
Devangi Hitenkumar Patel, Kamya Premal Shah, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Bogdan Constantin Neagu, Simo Attila, Fayez Alqahtani, Amr Tolba
Summary: Soil is crucial for agricultural produce quality and yield. This research proposes a crop recommendation algorithm based on soil attributes, which utilizes real-time data collected by soil sensors and validates the data using blockchain technology. The results are displayed on a user dashboard, allowing farmers to monitor their farm practices and sensor status remotely.
Article
Chemistry, Analytical
Navod Neranjan Thilakarathne, Muhammad Saifullah Abu Bakar, Pg Emerolylariffion Abas, Hayati Yassin
Summary: Modern agriculture utilizes technology, including artificial intelligence (AI) and machine learning (ML), to meet the demands of food production. AI assists in decision making and ML aids in crop recommendations, promoting the development and adoption of precision farming solutions.
Article
Agriculture, Multidisciplinary
Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava, Youcef Djenouri
Summary: In agricultural production, the stable application of nutrients and the use of genetic algorithms can increase crop fertility and yield. By analyzing crop fertility and yield, predicting suitable nutrients for different crops, and providing nutrient recommendations. The final nutrient recommendation is made by comparing recommended patterns with real-time sensor data.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Fatma M. Talaat
Summary: Agriculture faces a significant challenge in predicting crop yields. This paper presents a novel approach, utilizing IoT techniques in precision agriculture, to predict crop yields. The study trains and verifies machine learning models, achieving high scores in accuracy.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Plant Sciences
Debabrata Singh, Anil Kumar Biswal, Debabrata Samanta, Vijendra Singh, Seifedine Kadry, Awais Khan, Yunyoung Nam
Summary: The Internet of Things (IoT)-based smart farming offers fast and real-time response. Precision farming enabled by IoT can increase efficiency and output, while reducing water usage. IoT devices can assist farmers in monitoring crop health and development, automating tasks, and optimizing harvest times. This paper presents a smart framework for growing tomatoes using IoT devices and modules. It includes forecasting soil moisture levels, fine-tuning watering schedules, and providing crucial data through a smartphone app. Large-scale experiments validate the model's ability to intelligently monitor the irrigation system, leading to higher tomato yields.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Fisheries
Iva Guidini Lopes, Lucas Boscov Braos, Mara Cristina Pessoa Cruz, Rose Meire Vidotti
Summary: The study evaluated windrow composting as a sustainable solution for managing waste materials generated from aquaculture production. By converting waste into nutrient-rich composts, it is possible to achieve circularity and replace chemical fertilizers in new productive processes like agriculture.
Article
Agronomy
Francisco Puig, Juan Antonio Rodriguez Diaz, Maria Auxiliadora Soriano
Summary: Smart irrigation is becoming increasingly important in agriculture, but implementing it can be challenging for farmers. We have developed a low-cost, open-source IoT system for smart irrigation that can be integrated with other platforms and supports a wide range of sensors. The system has been tested on an olive farm, and the results show the advantages of using these technologies over traditional methods.
Article
Computer Science, Theory & Methods
Anusha Vangala, Ashok Kumar Das, Ankush Mitra, Sajal K. Das, Youngho Park
Summary: Precision farming has positive potential in water conservation, increased productivity, rural area development, and increased income. Blockchain technology provides a reliable and transparent alternative for storing and sharing farm data. Security systems are necessary for remote monitoring of agricultural fields to ensure information exchange only between authenticated entities.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Plant Sciences
Navod Neranjan Thilakarathne, Muhammad Saifullah Abu Bakar, Pg Emerolylariffion Abas, Hayati Yassin
Summary: Agriculture, as the primary and oldest industry in the world, has undergone transformation from the prehistoric era to the technology-driven 21(st) century. The advent of Information and Communication Technologies (ICTs) and the Internet of Things (IoT) has revolutionized the agricultural sector and shifted it from statistical to quantitative techniques. Farmers can now monitor crop conditions in real time and automate tasks using IoT solutions, leading to increased productivity and higher harvests.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Business
Gabriela Scur, Andre Victor Duarte da Silva, Claudia Aparecida Mattos, Rodrigo Franco Goncalves
Summary: This study fills a literature gap on the relationship between agricultural modernization and the internet of things (IoT), focusing on the adoption of IoT in vegetable crop cultivation. The study constructs a theoretical framework incorporating individual and organizational technology adoption models, and identifies the factors influencing IoT adoption. In-depth interviews with producers, experts, and suppliers were conducted, showing that only established producers implemented technology, highlighting the decisive role of organizational factors in IoT adoption.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Computer Science, Artificial Intelligence
Dekera Kenneth Kwaghtyo, Christopher Ifeanyi Eke
Summary: This article provides a comprehensive survey of existing smart farming models for precision agriculture, focusing on machine learning approaches and innovations in predicting and optimizing agricultural practices. The article highlights the poor performance of certain models due to issues with the dataset used and negligence in the pre-processing and feature extraction stages. It demonstrates how machine learning can automate agricultural practices, enhance crop quantity and quality, and reduce human labor. The challenges and prospects of smart farming models are also outlined for further exploration by researchers.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Ecology
Showkat Ahmad Bhat, Imtiyaz Hussain, Nen-Fu Huang
Summary: The main reason for agricultural productivity decline is farmers' failure to choose the appropriate crop for their soil. To address this issue, a crop selection system has been developed using GBRT-based deep learning surrogate models. The system determines the optimal hyperparameters using a Bayesian optimization algorithm and evaluates the impact of each input parameter using explainable artificial intelligence. The developed surrogate model achieves high accuracy and reliability, with an F1-Score of 1.0 for all classes in the dataset.
ECOLOGICAL INFORMATICS
(2023)
Article
Agronomy
Christine Musanase, Anthony Vodacek, Damien Hanyurwimfura, Alfred Uwitonze, Innocent Kabandana
Summary: This study presents an integrated crop and fertilizer recommendation system that utilizes machine learning and IoT to optimize agricultural practices in Rwanda. By using neural network and rule-based models, the system is able to accurately recommend crops and fertilizers, thus improving agricultural productivity.
Review
Plant Sciences
Elizabeth French, Ian Kaplan, Anjali Lyer-Pascuzzi, Cindy H. Nakatsu, Laramy Enders
Summary: Efforts to characterize soil, plant, and insect-associated microbial communities have revealed the complexity of crop-associated microbiomes. Plant-associated microorganisms have potential in improving agricultural sustainability, but research faces challenges in harnessing the beneficial properties of agricultural microbiomes for crop performance. Enhancing microbiome manipulation is a key strategy in achieving precision microbiome management for diverse agricultural systems.
Article
Agronomy
Angela Puig-Sirera, Marco Acutis, Marialaura Bancheri, Antonello Bonfante, Marco Botta, Roberto De Mascellis, Nadia Orefice, Alessia Perego, Mario Russo, Anna Tedeschi, Antonio Troccoli, Angelo Basile
Summary: This study uses the ARMOSA model to assess the effects of tillage and no-tillage practices on durum-wheat-cropping systems. The results show that no-tillage can increase wheat yield, increase soil organic carbon, and reduce nitrogen leaching, suggesting that it is a more resilient soil-management practice.
