Paper Reading AI Learner

Deployment of AGRI-BOT in Greenhouse Administration

2022-06-15 03:01:36
Ruchita Bhadre, Prathamesh Yeole

Abstract

Modern agriculture is constantly evolving to increase production despite unfavorable environmental conditions. A promising approach is 'greenhouse cultivation' providing a microclimate to the cultivated plants to overcome unfavorable climate. However, massive-sized greenhouses develop non-uniform micro-climate throughout the complex requiring high degree of human supervision. We propose deploying an Agri-Bot to create and maintain positive ecological conditions in the greenhouse, reducing labor costs and increasing production. The prototype will contain two primary systems, the navigation system and the data analytics system. The navigation system will be controlled by an Arduino, and data analytics will be handled using an ESP8266 microchip. Numerous sensors for measuring the greenhouse parameters will be mounted on the robot. It will follow a predefined path, while taking readings at checkpoints. The microchip will collect and process data from sensors, transmit to the cloud, and give commands to the actuators. The soil and climate parameters like temperature, humidity, light intensity, soil moisture, pH will be measured periodically. When the parameters are not within a specified range, the Agri-Bot will take corrective actions like switching on blowers/heaters, starting irrigation etc. If external intervention is required, eg., fertilizer, it will indicate accordingly. Deploying such an Agri-Bot for monitoring and controlling microclimate in large-scale greenhouses can mitigate labor costs while increasing productivity. In spite of an initial cost, it can provide a high return on investment by providing flexibility, low power consumption and easy management to help greenhouse be water efficient, provide evenly dispersed and controlled sunlight intensity, temperature and humidity.

Abstract (translated)

URL

https://arxiv.org/abs/2206.07266

PDF

https://arxiv.org/pdf/2206.07266.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot