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Article
Horticulture
Khaled Belouz et al.
Summary: In this study, artificial neural networks (ANNs) were used to predict greenhouse tomato yield and identify the most significant inputs for tomato production. Results showed that ANN models outperformed multiple linear regression (MLR) technique in terms of accuracy. Sensitivity analysis revealed insecticides, farmyard manure (FYM), potassium (K2O), nitrogen (N), electricity and fungicides as the most influential inputs in greenhouse tomato production.
SCIENTIA HORTICULTURAE
(2022)
Article
Chemistry, Analytical
Prabhjot Kaur et al.
Summary: Agriculture is vital to the economic development of India, and plant diseases can severely affect food safety and agricultural production. Automatic characterization of plant diseases is necessary due to the challenges of manual monitoring. This study successfully detects four common diseases in grape plants using transfer learning and machine learning algorithms.
Article
Agriculture, Multidisciplinary
Anand Muni Mishra et al.
Summary: This research proposes a deep convolutional neural network approach to identify weed density in soybean crop fields, achieving high accuracy in weed recognition.
JOURNAL OF PLANT DISEASES AND PROTECTION
(2022)
Article
Chemistry, Analytical
Seongho Jeong et al.
Summary: Due to climate change, plant diseases and pests are spreading at a faster rate, with the tomato leaf miner being a major concern. Regular inspections throughout the tomato's life cycle are necessary to prevent losses caused by the tomato leaf miner. Research on deep neural network models for detecting the tomato leaf miner found that the segmentation model outperformed the classification model, providing higher precision, recall, and F1-score values.
Article
Agronomy
Roei Grimberg et al.
Summary: This study evaluated deep-neural networks and machine learning models as tools for estimating foliage temperature. The machine learning model outperformed DNN in both all available features and high-correlated features scenarios.
Article
Green & Sustainable Science & Technology
Shu-Chu Liu et al.
Summary: This study aims to develop a novel crop harvest time prediction model integrating feature selection and artificial intelligence methods to accurately predict harvest time and reduce resource waste. The results show that the proposed model is more accurate than traditional methods, and prediction accuracy improves as the harvest time approaches.
Article
Computer Science, Theory & Methods
Dheo Prasetyo Nugroho et al.
Summary: Examination of the technological development in agriculture suggests that there is a lack of applications using cameras to detect tomato ripeness, resulting in the need for manual determination. This research explores the use of faster region-based convolutional neural network, single shot multibox detector, and you only look once models to recognize or detect tomato ripeness, achieving high accuracy rates.
INTERNATIONAL JOURNAL OF FUZZY LOGIC AND INTELLIGENT SYSTEMS
(2022)
Article
Agricultural Engineering
Daichi Minagawa et al.
Summary: This study proposes a novel method to predict the harvesting time for individual tomato fruits using image recognition and ripeness determination. The experimental results demonstrate the effectiveness of the method.
Article
Computer Science, Artificial Intelligence
Thippa Reddy Gadekallu et al.
Summary: With the rapid growth of human population, ensuring a healthy food supply and combating plant diseases threatening crop yield become crucial. This study focuses on using machine learning models to classify tomato disease images, extract features using optimization algorithms, apply deep neural networks for classification, and evaluate the model performance based on accuracy and loss rate metrics.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Wanhyun Cho et al.
Summary: This paper explores the prediction of how various environmental factors affect tomato yields by combining traditional statistical time series models and deep learning models. The proposed method predicts the response value more efficiently and better than existing models, with internal temperature, internal humidity, and CO2 level found to greatly affect tomato yields.
Article
Agricultural Engineering
Arthur Z. da Costa et al.
BIOSYSTEMS ENGINEERING
(2020)
Article
Mathematical & Computational Biology
Tao Sun et al.
STATISTICS IN MEDICINE
(2020)
Article
Plant Sciences
Manya Afonso et al.
FRONTIERS IN PLANT SCIENCE
(2020)
Article
Computer Science, Theory & Methods
John T. Hancock et al.
JOURNAL OF BIG DATA
(2020)
Article
Computer Science, Information Systems
Mayar Haggag et al.
Review
Multidisciplinary Sciences
Yann LeCun et al.
Article
Computer Science, Artificial Intelligence
UA Kumar
EXPERT SYSTEMS WITH APPLICATIONS
(2005)
Article
Computer Science, Artificial Intelligence
L Breiman
