Screening exotic groundnut (&lt;em&gt;Arachis hypogaea&lt;/em&gt; L.) lines for introducing as a small-seeded variety (ANKGN4/Tiny) in Sri Lanka

Jeewani, Diddugodage Chamila; Amarasinghe, Yaddehige Priya Jayantha; Wijesinghe, G.; Kumara, Rajapaksha Weeraman Pushpa

doi:10.4038/tare.v24i4.5543

Cited by 2 publications

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“…Groundnuts are primarily grown in the dry and intermediate zones of Sri Lanka, either as rainfed crops in highland areas during the Maha season or as irrigated crops in paddy lands during the Yala season. In Sri Lanka, the main groundnut cultivation regions include Moneragala, Kurunegala, Ampara, Badulla, Puttalama, and Ratnapura districts [60,61]. In 2021, the country's groundnut production reached 36,947 metric tons, cultivated across an area spanning 18,537 hectares [62].…”

Section: Introduction and Reviewmentioning

confidence: 99%

Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers

2024

JEESc

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Groundnut is an oil seed crop, which is grown widely in the country, and approximately 80% of groundnut is produced in the rainfed condition. Unlike weather factors even the prices of agriculture commodities are volatile in nature and the groundnut prices also behaves in an unusual pattern. A traditional farmer faces recurrent challenges. Deep learning methods and the accessibility of satellite imagery have, however, created new opportunities for more accurate and effective agricultural yield estimates. Large-scale yield estimation and understanding the impact of the variability of agricultural growing circumstances are critical due to the increased frequency of extreme climate occurrences. Crop growth condition models can be utilized with time series of spatially explicit information from satellite remote sensing (RS). For efficient agricultural management, guaranteeing food security, and making wise decisions about resource allocation and market forecasting, accurate and timely estimation of crop yields is essential. Crop yield estimation has historically depended on time-consuming field surveys and statistical models. Machine Learning (ML) is an exciting application of Artificial Intelligence. It provides the ability to learn by experiences without any explicit. The proposed model is based on simple\and cost-effective hardware that can be used by agriculture officers and farmers to get good productivity of crops. SCS model is trained by classifying dataset and tested subsequently. The accuracy and performance of an ML classifier depend only on the type and size of the dataset. Crop selection by real-time sensing data and soil analysis attributes is a big contribution in research of smart agriculture. A model was proposed basing on three modules: crop selection, crop management, and crop maturity. It used parameters soil moisture, temperature, humidity, air pressure, and air quality with weather conditions for better crop selection and health monitoring. A real-time sensory data was used for analysis on Thing Speak application with KNN algorithm. Some data mining techniques are applied for data preprocessing and comparing real-time data with trained data for crop prediction. It also considered crop prices for crop prediction, listed on National Commodity and Derivative Exchange. The KNN classifier is applied for data analysis has focused on IoT-oriented agricultural methods for weather monitoring. The prediction methods are investigated for commercial and scientific perspectives, cost of IoT components, Think Speak application is used for data analysis. An Android application is also designed to intimate the farmers about required water level of fields. IoT framework best use of land to improve farming methods and increase crop production with profit maximization. A wireless sensor network was deployed in the field to sense data for different parameters and for proper monitoring of field. It proposed a crop prediction method for crop yield maximization and quality of crops by considering real-time data of metrological factors using ML algorithms: precipitation, temperature, humidity, and solar light. Soft computing techniques can be employed to estimate the yield of various crops. As a result of rapid advancements in technology, crop models and decision tools have emerged as vital components of precision agriculture worldwide. These models and tools utilize linear regression techniques, nonlinear simulations, expert systems, Adaptive Neuro-Fuzzy Interference Systems, Support Vector Machines, Data Mining, Genetic Programming, and Artificial Neural Network (ANN) to predict harvest outcomes particularly under the influence of climate change. These prediction methods play a significant role in improving the accuracy and reliability of yield estimation in agricultural systems. ANNs successfully address identification, classification, and regression challenges in crop disease identification, harvest mechanization and product quality sorting. Multiple linear regression and discriminant function analysis were employed to construct a groundnut yield forecasting model, utilizing weather indices including maximum temperature, minimum temperature, total rainfall, morning relative humidity, and evening relative humidity. Employing techniques such as stepwise multiple linear regression, principal component analysis was combined with stepwise multiple linear regression, ANN, and penalized regressions like least absolute shrinkage and selection operator and elastic net. The models, particularly least absolute shrinkage and selection operator and elastic net, demonstrated remarkable accuracy, boasting a normalized root mean square error of under 10% across most test locations. Farmers using traditional methods in agriculture face problems such as low crop yield due to unpredicted weather, wrong amount of water and nutrients, and wrong selection of crop. In previous research work, limited parameters were used that are insufficient for high yield of crops. Our research is aimed at maximizing the crop yield by selecting suitable crop. We tackle this issue by applying technology methodically and evidencebased analysis. For instance, adding required amount of nutrients gives improved yields. Our work is based on selection of the influential parameters. Deep learning techniques used to give improved accuracy with less computational cost as compared to previous research.

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Section: Introduction and Reviewmentioning

confidence: 99%

Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers

2024

JEESc

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“…Groundnuts are primarily grown in the dry and intermediate zones of Sri Lanka, either as rain-fed crops in highland areas during the Maha season or as irrigated crops in paddy lands during the Yala season. In Sri Lanka, the main groundnut cultivation regions include Moneragala, Kurunegala, Ampara, Badulla, Puttalama, and Ratnapura districts [7,8]. In 2021, the country's groundnut production reached 36,947 metric tons, cultivated across an area spanning 18,537 hectares [9].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data

Sajindra,

Abekoon,

Wimalasiri

et al. 2023

AgriEngineering

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Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for their performance using climatic factors such as minimum temperature, maximum temperature, and rainfall in different regions of Sri Lanka, considering the seasonal variations in groundnut yield. A three-layer neural network was employed, comprising a hidden layer. The hidden layer consisted of 10 neurons, and the log sigmoid functions were used as the activation function. The performance of these configurations was evaluated based on the mean squared error and Pearson correlation. Notable improvements were observed when using the Levenberg–Marquardt algorithm as the training algorithm and applying the natural logarithm transformation to the yield values. These improvements were evident through the higher Pearson correlation values for training (0.84), validation (1.00) and testing (1.00), and a lower mean squared error (2.2859 × 10−21) value. Due to the limited data, K-Fold cross-validation was utilized for optimization, with a K value of 5 utilized for the process. The application of the natural logarithm transformation to the yield values resulted in a lower mean squared error (0.3724) value. The results revealed that the Levenberg–Marquardt training algorithm performs better in capturing the relationships between the climatic factors and groundnut yield. This research provides valuable insights into the utilization of climatic factors for predicting groundnut yield, highlighting the effectiveness of the training algorithms and emphasizing the importance of carefully selecting and expanding the climatic factors in the modeling equation.

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Screening exotic groundnut (<em>Arachis hypogaea</em> L.) lines for introducing as a small-seeded variety (ANKGN4/Tiny) in Sri Lanka

Cited by 2 publications

References 0 publications

Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers

Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers

An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data

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