Artificial Intelligence based Crop Recommendation System

Jagadeeswari, M.; Manikandababu, C. S.; Kavya, S; Prasanna, R; Madumitha, S

doi:10.1109/icirca54612.2022.9985645

Cited by 4 publications

(1 citation statement)

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“…ML with IoT can help in reducing water usage with minimal intervention of farmers [15]. Multilayer perceptron (MLP) classifier for the crop prediction system is proposed by the author, where the random forest (RF) algorithm has been implemented [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Smart irrigation with crop recommendation using machine learning approach

Palakshappa,

Kyathanahalli Nanjappa,

Mahadevappa

et al. 2024

Bulletin EEI

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Increasing crop yield with sustainable growth is the primary requirement for farmers with a growing population. Effective management and conservation of depleting natural resources is a priority task. Decrease in manpower due to migrating population has forced automation in agriculture. In this work, an automatic water irrigation and an effective crop recommendation system is proposed. Gypsum blocks based soil sensor is used to measure dielectric permittivity associated with the tested soil. The water-potential present in soil, along with potassium (K), nitrogen (N), phosphorus (P), potential of hydrogen (pH) helps to quantify the soil nutrients available and the suitable crop that can be considered for harvesting in a specified demography and environment. Sensory data indicating soil quality obtained is used to recommend crops by utilizing machine learning approaches. Telegram application is linked to the recommendation model to assist decision making and to ensure farmer-friendliness by sending notifications periodically.

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Section: Introductionmentioning

confidence: 99%

Smart irrigation with crop recommendation using machine learning approach

Palakshappa,

Kyathanahalli Nanjappa,

Mahadevappa

et al. 2024

Bulletin EEI

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AI-Optimized Irrigation for Sustainable Agriculture

Hussain,

Maurya

et al. 2024

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

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Kho Yolo Net

Subramani,

Suganthi

2025

Advances in Marketing, Customer Relationship Management, and E-Services

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India's population largely depends on agriculture for their livelihood, making agricultural research vital for the nation's economy. Plant leaf disease diagnosis is essential for ensuring healthy crop production and minimizing financial losses, as plant diseases can significantly reduce crop yield and quality. Machine learning (ML), particularly image-based analysis, has revolutionized plant disease detection. To address plant leaf disease identification via image analysis, a novel KHO-YOLO Net technique has been developed. Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to plant leaf images to improve their quality. YOLO v8 is used to detect multiple diseases within a single image. YOLOv8 is optimized using the Krill Herd Optimization (KHO) technique for superior classification outcomes. The efficacy of the proposed system is compared to YOLOv3, YOLOv4, YOLOv5, and YOLOv6, with the proposed YOLOv8 demonstrating an overall accuracy improvement to 99.59%.

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Artificial Intelligence based Crop Recommendation System

Cited by 4 publications

References 10 publications

Smart irrigation with crop recommendation using machine learning approach

Smart irrigation with crop recommendation using machine learning approach

AI-Optimized Irrigation for Sustainable Agriculture

Kho Yolo Net

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