Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes

Agriculture faces a significant challenge in predicting crop yields, a critical aspect of decision-making at international, regional, and local levels. Crop yield prediction utilizes soil, climatic, environmental, and crop traits extracted via decision support algorithms. This paper presents a novel...

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Vydáno v:Neural computing & applications Ročník 35; číslo 23; s. 17281 - 17292
Hlavní autor: Talaat, Fatma M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.08.2023
Springer Nature B.V
Témata:
Active learning
Agricultural production
Agriculture
Algorithms
Artificial Intelligence
Big Data
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Crop yield
Data Mining and Knowledge Discovery
Decision making
Image Processing and Computer Vision
Machine learning
Meteorology
Original Article
Pests
Probability and Statistics in Computer Science
Precision agriculture
Crop yield prediction
Active learning
IoT
ISSN:0941-0643, 1433-3058
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Shrnutí:Agriculture faces a significant challenge in predicting crop yields, a critical aspect of decision-making at international, regional, and local levels. Crop yield prediction utilizes soil, climatic, environmental, and crop traits extracted via decision support algorithms. This paper presents a novel approach, the Crop Yield Prediction Algorithm (CYPA), utilizing IoT techniques in precision agriculture. Crop yield simulations simplify the comprehension of cumulative impacts of field variables such as water and nutrient deficits, pests, and illnesses during the growing season. Big data databases accommodate multiple characteristics indefinitely in time and space and can aid in the analysis of meteorology, technology, soils, and plant species characterization. The proposed CYPA incorporates climate, weather, agricultural yield, and chemical data to facilitate the anticipation of annual crop yields by policymakers and farmers in their country. The study trains and verifies five models using optimal hyper-parameter settings for each machine learning technique. The DecisionTreeRegressor achieved a score of 0.9814, RandomForestRegressor scored 0.9903, and ExtraTreeRegressor scored 0.9933. Additionally, we introduce a new algorithm based on active learning, which can enhance CYPA's performance by reducing the number of labeled data needed for training. Incorporating active learning into CYPA can improve the efficiency and accuracy of crop yield prediction, thereby enhancing decision-making at international, regional, and local levels.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08619-5