Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

Madhuri, J.; Indiramma, M

doi:10.17485/ijst/v14i19.64

Cited by 35 publications

(7 citation statements)

References 26 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The difference between forecasts and estimates is that forecasts are created before the full crop is harvested, whereas estimates are made after the crop is harvested. Farmers have always made "forecasts" in order to organise their agronomic methods [9], and this has been true throughout history. For example, the planting window, the selection of a cultivar, and the amount of fertiliser to be used are all influenced by the climate in question.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Plantosphere: Next Generation Adaptive and Smart Agriculture System

et al. 2022

View full text Add to dashboard Cite

Around 75% of the population in India is engaged in agriculture and farming. The sustainability of every economy is based on agriculture. It has a major influence on financial growth and fundamental transformation in the long run. Artificial intelligence will usher in a revolution in agricultural operations in the future. This revolution has protected crops from being negatively affected by a variety of factors such as climate change, soil porosity, and water availability. Crop monitoring, soil management, and insect identification, to name a few examples, are all conceivable uses of artificial intelligence in agriculture. The primary purpose of artificial intelligence is to close the knowledge gap that exists between inventors and farmers. Detecting disease and monitoring plant health are two of the most difficult challenges in sustainable farming. As a result, image processing technology must be used to detect plant sickness at an early stage. Photographic capture, preprocessing, segmentation, feature extraction, and sickness categorization are all part of the procedure. In reality, computer image processing was used long before human eyes were able to detect the signs and symptoms of the disease. Taking into account the climatic conditions in various parts of the world. Climate change directly affects crop output. Several soil and atmospheric characteristics are detected to anticipate the optimal crop. Sedimentation is measured by soil parameters such as pH and moisture. Today, a platform that allows farmers to advertise their products is in high demand. This paper proposes a system where farmers sell directly to clients, bypassing wholesalers and traders. A predictive analytics solution is required to maximize the farmer’s profit.

show abstract

Section: Journal Of Sensorsmentioning

confidence: 99%

“…An RGB image of a tomato with yellow leaf curl disease is used to identify the illness [9,10]. This method's average accuracy was 90%.…”

mentioning

confidence: 99%

Plantosphere: Next Generation Adaptive and Smart Agriculture System

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Choudhary M. et al [29] propose a machine learning-based plant disease prediction and crop recommendation system. Madhuri and M Indiramma [30] propose an Artificial Neural Network (ANN) based crop recommendation system according to the climatic conditions, soil type, and crop characteristics. The proposed crop recommendation is a promising aspect of crop planning with an accuracy of 96% in predicting the crop type.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

et al. 2022

View full text Add to dashboard Cite

An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, timeconsuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and -0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.INDEX TERMS Internet of Things (IoT), machine learning, soil fertility mapping, fertilizer recommendation, support vector machine (SVM), Gaussian Naïve Bayes (GNB), logistic regression (LR), k-nearest neighbor (KNN).

show abstract

“…Crop recommendation is a technique formulated to deal with these issues and obtain a solution to improve the net worth of agricultural sectors. 6 The traditional technique of crop recommendation includes the farmer's decisions, which are ultimately intuitive and may lead to wrong predictions and cultivations. 7,8 It influences the net growth rate of the crops and thereby reduces productivity to a large extent.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the crop yield, it is necessary to predict the exact crops that can be cultivated for a particular period. Crop recommendation is a technique formulated to deal with these issues and obtain a solution to improve the net worth of agricultural sectors 6 . The traditional technique of crop recommendation includes the farmer's decisions, which are ultimately intuitive and may lead to wrong predictions and cultivations 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep‐Q Elman neural network for crop prediction and recommendation based on environmental changes

Shingade

Mudhalwadkar

2022

Concurrency and Computation

View full text Add to dashboard Cite

Crop recommendation is a potential research topic that relies on environmental conditions such as temperature, humidity, rainfall, and soil pH to identify suitable crops for cultivation. There are diverse models available in the literature for crop recommendation. Still, those models are not accurate enough to predict the appropriate crop when there is a sudden change in the environmental factors. The models cannot map the raw data exactly with the prediction values, and the output relies on the quality of the input features used. To resolve these issues, the concept of Q‐learning is hybridized with deep learning to enable exact mapping of the raw data with the prediction values. In this article, Q‐learning is combined with the Elman neural network, trained with the input parameters selected by the improved Archimedes optimization algorithm from the dataset. The model evaluations are carried out with the user dataset constructed using the sensor information collected from the regions of Maharashtra. The overall accuracy provided by the proposed crop recommendation model is 99.44%, and the average inference time of the model is 0.0117 s.

show abstract

Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

Cited by 35 publications

References 26 publications

Plantosphere: Next Generation Adaptive and Smart Agriculture System

Plantosphere: Next Generation Adaptive and Smart Agriculture System

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

Hybrid deep‐Q Elman neural network for crop prediction and recommendation based on environmental changes

Contact Info

Product

Resources

About