Background: India faces a critical shortage of farm labour. Out of basic farming operations, weeding operations are not fully mechanized in India. Traditional weed control methods are tedious, labour-intensive and costly. 
 
Methods: The objective of this study was to develop a prototype of an autonomous weeder for inter and intra-row weeding. The materials were used include a DC motor operating on electric rechargeable batteries, a camera for taking photographs of the intra-row space, an inter-and intra-row weeding tool assembly, a Raspberry Pi to deploy a machine learning object detection model to detect weeds and crops, to control a motor and to control an intra-row weeding tool through the pneumatic actuator. While the intensive image dataset was prepared from a cultivated spring onion farm on a test soil bin over the University campus, moreover the small-sized YOLOv8n model worked accurately on the Raspberry Pi. 
 
Result: YOLOv8n achieved a mean average precision (mAP) of 0.85 at the Intersection of the Union (IoU) threshold of 50%. The results indicated that the prototype removed both types of weeds with an overall weeding efficiency of 86%, crop damage of 4%, a field capacity of 0.023 hectares per hour and a performance index 1726.26 using 0.11 horsepower. It was concluded that the study contributed a spring onion-weed dataset and a Weeding Agrobot prototype to test the described concept, which would help farmers reduce labour dependency and the cost of cultivation. 
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Development of a Weeding Agrobot Prototype for Inter-row and Intra-row WeedingDevelopment of a Weeding Agrobot Prototype for Inter-row and Intra-row Weeding

Background: Soybean, a crucial oilseed crop in India, is susceptible to various diseases, including Yellow Mosaic Disease caused by Mungbean Yellow Mosaic Virus (MYMV). Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crop. Therefore, this study aims to evaluate the efficacy of YOLOv8 for detecting Mungbean Yellow Mosaic Virus in soybean crops. 
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Methods: Images were captured using 48 MP mobile camera from the experimental fields of Main Agricultural Research Station, University of Agricultural Sciences, Dharwad during 2024 summer season. The captured images were annotated, augmented using various transformations, resized and normalized. As a result, a dataset of 4480 annotated images of MYMV-infected soybean plants was created. The dataset was randomly split into training and validation set. Different variants of YOLOv8 models were trained and evaluated based on precision, recall, mean Average Precision (mAP) and inference time. 
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Result: Among the variants, YOLOv8x demonstrated superior performance, with a precision of 98.1%. Whereas the recall (78.6%), mAP@0.5 (85.3%) and F1-score (86.9%) were higher in YOLOv8s. YOLOv8n boasted the lowest inference time of 3.6 milliseconds and the least number of model parameters (3.1 millions). YOLOv8s shows promise as an efficient and accurate tool for MYMV detection in soybean crop. Its low inference time and high precision make it suitable for real-world deployment, aiding in early disease detection and precise management of the disease. This study contributes to advancing disease detection techniques in agriculture, facilitating early intervention and minimizing crop losses for sustainable agricultural practices.

Development of a Weeding Agrobot Prototype for Inter-row and Intra-row WeedingDevelopment of a Weeding Agrobot Prototype for Inter-row and Intra-row Weeding

Artificial Intelligence based Precise Disease Detection in Soybean using Real Time Object Detectors