Prediction of corn crop yield using backpropagation neural network algorithm

Putro, Sigit Susanto; Syakur, Muhammad Ali; Rochman, Eka Mala Sari; Musfirotummamlu'ah,; Rachmad, Aeri

doi:10.1063/5.0127695

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The features are then combined from the color channel's hue, saturation, and value (H, S, and V). The classification method used is Random Forest with the following parameters: bootstrap = True, estimators = (10, 30, 50, 70, 90, 100), max depth = (3,5,7,9,10), min_samples_split = (2,4,6), and k-fold = 10. The results of the testing conducted in this research are as follows:…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

Aeri Rachmad,

Nachnul Ansori,

Silfia Rifka

et al. 2023

Technium

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Corn disease has a significant impact on both the food industry and the yield of corn crops since corn serves as a fundamental and essential source of nutrition, especially for vegetarians and vegans. Therefore, ensuring the quality of corn is crucial, and to achieve this, protection against various diseases is necessary. Consequently, there is a pressing demand for an automated method capable of early-stage disease detection and prompt action. However, detecting diseases at an early stage poses a major challenge and is of utmost importance. This research focuses on the development of a classification model for corn stalk images using Random Forest. The model generates fine and coarse features of high quality to capture discriminative, boundary, pattern, and structural information used in the classification process. This research also utilizes the LBP (Local Binary Pattern) method and Color Histogram in the feature extraction process to obtain information related to texture and distinguishing patterns, that are employed in the classification process. Furthermore, the proposed model is evaluated using the corn plant image dataset, which was directly captured by the researcher in Madura, and consists of 3,000 data. The result of this research shows that the utilization of the proposed method can classify and identifying diseases in new data of digital images of corn stalks with an accuracy rate of 99.05%.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Certain crops hold significant economic importance, both domestically and through exportation. Among them, corn stands out as a crucial crop in the agricultural sector [3]. Corn serves as a traditional crop with diverse uses, including human food, animal feed, and raw materials for multiple industries [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

Aeri Rachmad,

Nachnul Ansori,

Silfia Rifka

et al. 2023

Technium

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“…However, the demand for maize is continuously increasing both for food and industrial raw materials, resulting in unmet needs [7].…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Fertilizing Maize Plants Through the Application of Slow Release NPK Tablet Fertilizer with Biofertilizer

Novianti,

Syaiful,

Dachlan

et al. 2024

Indonesian J. Agric. Res

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Inefficiencies in fertilization practices have become a substantial issue within current agricultural techniques. The inappropriate use of fertilizers can negatively impact both crop productivity and soil fertility. The aim of this research is to identify the efficiency of utilizing slow-release NPK tablet fertilizers supplemented with biofertilizers in maize crops. The experimental design incorporated a randomized complete block design (RCBD) consisting of nine combinations of fertilizer dosages between NPK tablet fertilizers and biofertilizers. The efficiency of fertilizer use can be seen from the RAE value of more than 100% shown by the NPK Tablet treatment which requires only one application compared to the recommended fertilizer, urea, and NPK Phonska which requires twice applications. Furthermore, optimization of the application of biofertilizer can be seen in the RSE value of more than 100% shown in the application of LBA biofertilizer together with NPK Tablets so that the application of biofertilizer is considered capable of increasing the efficiency of using inorganic fertilizers such as NPK Tablets.

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“…If there is a decrease in the amount of production, this can affect market share which has a negative impact on society, especially in Indonesia, which is a country with a high level of corn consumers. There are several factors that cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged resulting in the phenomenon of crop failure [2,3].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Rachmad¹,

Fuad²,

Rochman³

2023

MMEP

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The decline in corn production can affect the continuity of food grown in society, especially in Indonesia, which is a country with a high level of corn consumers. Several factors cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged. Therefore, a system is needed that can identify diseases in corn plants so that appropriate treatment can be carried out as early as possible to prevent severe damage to corn plants. With this research, the system can be built by utilizing machine learning in building a classification system using the Convolutional Neural Network (CNN) algorithm with a dataset of corn leaf images taken from farmers' fields in the Madura Region with four target classes namely healthy, gray leaf spot, blight, and common rust. Testing was carried out using several CNN architectural models such as SqueezeNet, AlexNet, ResNet-101, ResNet-50, and ResNet-18. The parameters used were 5 epochs with 100 iterations, a learning rate of 0.0001, using Adam optimization, and a data distribution of 70% for training data and 30% for testing data. The test results obtained in classifying corn images using the Convolutional Neural Network method with the ResNet-50 architecture provide a very good accuracy value of 95.59%.

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Prediction of corn crop yield using backpropagation neural network algorithm

Cited by 4 publications

References 7 publications

Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

Efficiency of Fertilizing Maize Plants Through the Application of Slow Release NPK Tablet Fertilizer with Biofertilizer

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

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