Overview of Crop Models for Assessment of Crop Production

Ritchie, J. T.; Alagarswamy, G.

doi:10.1007/978-1-4615-0969-1_3

Cited by 9 publications

(7 citation statements)

References 41 publications

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“…The formula used here for calculating the OB of any image is based on the Minkowski norm formula [28], and it is shown in Equation (2).…”

Section: Energy Calculation and Illumination Adjustment Processmentioning

confidence: 99%

“…In 2020, 119,833,677 tonnes of bananas were produced worldwide, of which 64,725,093 tonnes or 54.4% were produced in Asia. In Southeast Asia specifically, 32,859,278 tonnes of bananas were produced, with Thailand producing 1,360,670 tonnes [2]. Bananas are one of the most widely cultivated tropical fruits, and they can be grown on small-scale farms with low production costs, yielding the first harvests 14 months after planting and continuing for up to 10 years.…”

Section: Introductionmentioning

confidence: 99%

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Developing a Colorimetric Equation and a Colorimetric Model to Create a Smartphone Application That Identifies the Ripening Stage of Lady Finger Bananas in Thailand

Tanut,

Tatomwong,

Buachard

2023

Sensors

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This article develops a colorimetric equation and a colorimetric model to create a smartphone application that identifies the ripening stage of the lady finger banana (LFB) (Musa AA group ‘Kluai Khai’, กล้วยไข่ “gluay kai” in Thai). The mobile application photographs an LFB, automatically analyzes the color of the banana, and tells the user the number of days until the banana ripens and the number of days the banana will remain edible. The application is called the Automatic Banana Ripeness Indicator (ABRI, pronounced like “Aubrey”), and the rapid analysis that it provides is useful to anyone involved in the storage and distribution of bananas. The colorimetric equation interprets the skin color with the CIE L*a*b* color model in conjunction with the Pythagorean theorem. The colorimetric model has three parts. First, COCO-SSD object detection locates and identifies the banana in the image. Second, the Automatic Power-Law Transformation, developed here, adjusts the illumination to a standard derived from the average of a set of laboratory images. After removing the image background and converting the image to L*a*b*, the data are sent to the colorimetric equation to calculate the ripening stage. Results show that ABRI correctly detects a banana with 91.45% accuracy and the Automatic Power-Law Transformation correctly adjusts the image illumination with 95.72% accuracy. The colorimetric equation correctly identifies the ripening stage of all incoming images. ABRI is thus an accurate and robust tool that quickly, conveniently, and reliably provides the user with any LFB’s ripening stage and the remaining days for consumption.

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“…The formula used here for calculating the OB of any image is based on the Minkowski norm formula [28], and it is shown in Equation (2).…”

Section: Energy Calculation and Illumination Adjustment Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing a Colorimetric Equation and a Colorimetric Model to Create a Smartphone Application That Identifies the Ripening Stage of Lady Finger Bananas in Thailand

Tanut,

Tatomwong,

Buachard

2023

Sensors

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“…Agricultural production is fundamental from an economic point of view and to feed the world's population. It has been estimated that from 1961 to 2020, food production from agricultural sources has increased significantly, with current per capita values nearing almost 3200 kcal/day at the end of the supply chain [1]. Yet, the annual State of Food Security and Nutrition in the World (SOFI) report published by the Food and Agriculture Organization (FAO) showed that more than 700 million people faced hunger in 2022 [2].…”

Section: Introductionmentioning

confidence: 99%

To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches

Prieto-Martínez,

Mendoza-Cañas,

Martínez-Mayorga

2024

Computation

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Auxins are chemical compounds of wide interest, mostly due to their role in plant metabolism and development. Synthetic auxins have been used as herbicides for more than 75 years and low toxicity in humans is one of their most advantageous features. Extensive studies of natural and synthetic auxins have been made in an effort to understand their role in plant growth. However, molecular details of the binding and recognition process are still an open question. Herein, we present a comprehensive in silico pipeline for the assessment of TIR1 ligands using several structure-based methods. Our results suggest that subtle dynamics within the binding pocket arise from water–ligand interactions. We also show that this trait distinguishes effective binders. Finally, we construct a database of putative ligands and decoy compounds, which can aid further studies focusing on synthetic auxin design. To the best of our knowledge, this study is the first of its kind focusing on TIR1.

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“…The Mexican Ministry of Agriculture, Livestock, Rural Development, Fishing and Food (MALRDFF) through the AgriFood and Fisheries Information Service presented the report on Mexico's AgriFood Trade Balance, indicating that tomato is the second most exported agricultural product, with avocado taking the first place. Besides this, tomato production in Mexico has an annual variation of 5.3% from 2011 to 2020 [3]. However, production is affected by different circumstances.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Tomato Leaf Disease through Convolutional Neural Networks

Ibáñez

Reyes-Muñoz

2023

Electronics

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Agriculture plays an essential role in Mexico’s economy. The agricultural sector has a 2.5% share of Mexico’s gross domestic product. Specifically, tomatoes have become the country’s most exported agricultural product. That is why there is an increasing need to improve crop yields. One of the elements that can considerably affect crop productivity is diseases caused by agents such as bacteria, fungi, and viruses. However, the process of disease identification can be costly and, in many cases, time-consuming. Deep learning techniques have begun to be applied in the process of plant disease identification with promising results. In this paper, we propose a model based on convolutional neural networks to identify and classify tomato leaf diseases using a public dataset and complementing it with other photographs taken in the fields of the country. To avoid overfitting, generative adversarial networks were used to generate samples with the same characteristics as the training data. The results show that the proposed model achieves a high performance in the process of detection and classification of diseases in tomato leaves: the accuracy achieved is greater than 99% in both the training dataset and the test dataset.

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Overview of Crop Models for Assessment of Crop Production

Cited by 9 publications

References 41 publications

Developing a Colorimetric Equation and a Colorimetric Model to Create a Smartphone Application That Identifies the Ripening Stage of Lady Finger Bananas in Thailand

Developing a Colorimetric Equation and a Colorimetric Model to Create a Smartphone Application That Identifies the Ripening Stage of Lady Finger Bananas in Thailand

To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches

Monitoring Tomato Leaf Disease through Convolutional Neural Networks

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