Green products in the management of stored food grains: Challenges, recent advances and future prospects

Prakash, Bhanu; Singh, Prem Pratap; Raghuvanshi, Tanya Singh

doi:10.1016/b978-0-323-95590-4.00007-2

Cited by 2 publications

(1 citation statement)

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“…The importance of effective pest prediction in agriculture is strongly related to the pivotal role that grain crops play in global food security. Grain crops, including cereals, legumes, and oilseeds, serve as essential food sources for a substantial portion of the world's population [5]. However, the threat posed by pest infestations to these crops is significant, resulting in considerable economic losses and jeopardizing global food security.…”

Section: Introductionmentioning

confidence: 99%

Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan

Anarbekova,

Ruiz,

Akanova

et al. 2024

MAKE

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This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network for forecasting the population dynamics of Phyllotreta vittula pests in Kazakhstan. By evaluating various network configurations and hyperparameters, this research considers the application of MLP, MT-ANN, LSTM, transformer, and SVR. The transformer consistently demonstrates superior predictive accuracy in terms of MSE. Additionally, this work highlights the impact of several training hyperparameters such as epochs and batch size on predictive accuracy. Interestingly, the second season exhibits unique responses, stressing the effect of some features on model performance. By advancing our understanding of fine-tuning ANNs for accurate pest prediction in grain crops, this research contributes to the development of more precise and efficient pest control strategies. In addition, the consistent dominance of the transformer model makes it suitable for its implementation in practical applications. Finally, this work contributes to sustainable agricultural practices by promoting targeted interventions and potentially reducing reliance on chemical pesticides.

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Section: Introductionmentioning

confidence: 99%