Towards sustainable agriculture: Harnessing AI for global food security

Artificial Intelligence in Agriculture, Год журнала: 2024, Номер 12, С. 72 - 84

Опубликована: Апрель 30, 2024

The issue of food security continues to be a prominent global concern, affecting significant number individuals who experience the adverse effects hunger and malnutrition. finding solution this intricate necessitates implementation novel paradigm-shifting methodologies in agriculture sector. In recent times, domain artificial intelligence (AI) has emerged as potent tool capable instigating profound influence on sectors. AI technologies provide advantages by optimizing crop cultivation practices, enabling use predictive modelling precision techniques, aiding efficient monitoring disease identification. Additionally, potential optimize supply chain operations, storage management, transportation systems, quality assurance processes. It also tackles problem loss waste through post-harvest reduction, analytics, smart inventory management. This study highlights that how utilizing power AI, we could transform way produce, distribute, manage food, ultimately creating more secure sustainable future for all.

Язык: Английский

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Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming

Neural Computing and Applications, Год журнала: 2024, Номер 36(32), С. 20011 - 20025

Опубликована: Авг. 10, 2024

Язык: Английский

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Interpretability Research of Deep Learning: A Literature Survey

Information Fusion, Год журнала: 2024, Номер 115, С. 102721 - 102721

Опубликована: Окт. 9, 2024

Язык: Английский

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A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms

Agriculture, Год журнала: 2024, Номер 14(8), С. 1256 - 1256

Опубликована: Июль 30, 2024

Today, crop suggestions and necessary guidance have become a regular need for farmer. Farmers generally depend on their local agriculture officers regarding this, it may be difficult to obtain the right at time. Nowadays, datasets are available different websites in sector, they play crucial role suggesting suitable crops. So, decision support system that analyzes dataset using machine learning techniques can assist farmers making better choices selections. The main objective of this research is provide quick with more accurate effective recommendations by utilizing methods, global positioning coordinates, cloud data. Here, recommendation personalized, which enables predict crops specific geographical context, taking into account factors like climate, soil composition, water availability, conditions. In regard, an existing historical contains state, district, year, area-wise production rate, name, season was collected 246,091 sample records from Dataworld website, holds data 37 areas India. Also, analysis, offices Rayagada, Koraput, Gajapati districts Odisha Both these were combined stored Firebase service. Thirteen algorithms been applied identify dependencies within To facilitate process, Android application developed Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, SDK 33), Tools. A model has proposed implements SMOTE (Synthetic Minority Oversampling Technique) balance dataset, then allows implementation 13 classifiers, such as logistic regression, tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Classifier, Cat Boost, HGB (Histogram-based Boosting), SGDC (Stochastic Descent), MNB (Multinomial Naive Bayes) dataset. It observed performance method 1.00 accuracy, precision, recall, F1-score, ROC AUC (Receiver Operating Characteristics–Area Under Curve) 0.91 sensitivity 0.54 specificity after applying SMOTE. Overall, compared all other classifiers implemented predictions.

Язык: Английский

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Challenges in Achieving Artificial Intelligence in Agriculture

Опубликована: Янв. 1, 2024

Язык: Английский

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Incorporating soil information with machine learning for crop recommendation to improve agricultural output

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 12, 2025

The agriculture field is the basis of a country's change and financial system. Crops are main source revenue for people. One farmer's most challenging problems choosing right crops their land. This critical decision has direct impact on productivity profit. Wrong crop selection not only reduces yields but also causes food shortages, creating more farmers. best depends many parameters such as illustration humidity, N, K, P, pH, rainfall, temperature soil. Getting advice from experts an easy task. requires intelligent models in recommendations that use machine-learning to suggest suitable soil other environmental conditions. Temperature, pH important data growing agriculture. In this study, we gather preprocess relevant data. To recommend crop, propose novel ensemble learning approach called RFXG based random forest (RF) extreme gradient boosting (XGB) out twenty-two major crops. measure capability proposed approach, various machine utilized including extra tree classifier, multilayer perceptron, RF, trees, logistic regression, XGB classifiers. get performance, optimization hyperparameter, K-fold cross-validation procedures performed. Experimental outcomes show technique achieves recommendation accuracy 98%. Specifically, solution provides immediate help farmers make timely decisions.

Язык: Английский

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Customization of health insurance premiums using machine learning and explainable AI

International Journal of Information Management Data Insights, Год журнала: 2025, Номер 5(1), С. 100328 - 100328

Опубликована: Фев. 7, 2025

Язык: Английский

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 28, 2025

Язык: Английский

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A Multi‐Objective Decision‐Making Neural Network: Effective Structure and Learning Method

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(4-5)

Опубликована: Фев. 19, 2025

ABSTRACT Decision Neural Networks significantly improve the performance of complex models and create more transparent accountable decision‐making systems that can be trusted in critical applications. However, their strongly depends on amount data learning algorithm. This article describes development a simplified structure training algorithm based Levenberg–Marquardt to enhance decision neural network's assess utility function's efficacy multi‐objective issues. The suggested converges faster than traditional algorithms. Also, designed scheme combines gradient descent with Gauss‐Newton method, allowing it escape shallow local minima effectively other similar techniques. Numerical examples demonstrate how well method estimates linear functions, even complicated nonlinear ones. Additionally, findings applying enhanced network issues show this instructional technique produces responses higher quality convergence. By problem seven primary answers, is shown accuracy improved by 20%.

Язык: Английский

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Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects

Electronics, Год журнала: 2024, Номер 13(22), С. 4362 - 4362

Опубликована: Ноя. 7, 2024

This article explores the transformative potential of artificial intelligence (AI) tools across agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% 2050, AI technologies—including machine learning, big data analytics, Internet things (IoT)—offer critical solutions for enhancing productivity, sustainability, resource efficiency. The study provides a comprehensive review applications at multiple stages including land use planning, crop selection, management, disease detection, yield prediction, market integration. It also discusses significant challenges adoption, such as accessibility, technological infrastructure, need specialized skills. By examining case studies empirical evidence, demonstrates how AI-driven can optimize decision-making operational efficiency in agriculture. findings underscore AI’s pivotal role addressing with implications farmers, agribusinesses, policymakers, researchers. aims advance evolving research discussions on sustainable agriculture, contributing insights that promote adoption technologies influence farming.

Язык: Английский

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