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RISS 인기검색어
- 검색키워드
- 저자 : Machavaram Rajendra (검색결과 3 건)
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Need of Automation in Paddy Nurseries for Raising Paddy Seedlings in India: a Review
Choudhary Vinod,Machavaram Rajendra 한국농업기계학회 2022 바이오시스템공학 Vol.47 No.2
Purpose Paddy seedling raising is a time-consuming, laborious, and high-energy input operation in nurseries with a systematic approach, repetitive motion, and a well-suited structured environment. The semi-automatic paddy seedling preparation units are cumbersome due to the limitations on manual feeding of feed material for desired quantity, watering, discharging, and tray stacking with respect to work duration and skill of the worker. Automation in paddy seedling preparation has allowed the farmers for saving in labor, energy input, and time required for raising seedlings and also monitoring all the variables in uniform distribution of feeding material, viz., soil organic mixture, paddy seeds, watering, tray discharging, tray stacking, and growth environment under paddy nurseries. Methods Mat-type paddy seedling preparation using recent IoT or embedded electronic system-based technologies have been extensively surveyed for working on automatic tray discharging, tray stacking, and feed mechanisms to set up the desired quantity of material in paddy nurseries. Apart from this, we have reviewed different existing practices for paddy seedling preparation. Results The automated systems have helped ease the paddy seedling preparation operation, efficient vigor, and healthy seedlings growth by preserving the precision, accuracy, and effectiveness in raising paddy seedlings with minimal human interference. Conclusions This review highlights the research gaps and development in smart paddy seedling preparation technologies used in paddy transplanting with propermanagement and monitoring. The above advances will improve the efficiency of paddy seedling mat preparation to increase the quality and quantity of the product and pose an opportunity for the growth of the mat preparation market in the near future in paddy cultivation.
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Gupta Chanchal,Tewari V. K.,Machavaram Rajendra 한국농업기계학회 2022 바이오시스템공학 Vol.47 No.1
Purpose Chili is a spice cumvegetable crop popular for the production of dry chili powder, canned or frozen chili sauces, pickles, etc. However, its conventional manual harvesting practice is time-consuming method and the unavailability of labor during the picking season causes a delay in the harvesting period which directly impart poor-quality product. Methods In this study, a comb-type picking mechanism was developed for multiple-pass harvesting and the optimal working conditions were evaluated considering picking mechanism rotational speed and plant conveying speed. The comb-type picking mechanismwas designed by considering the physical and mechanical properties of chili cultivar and the laboratory setup consists of a plant conveying system, picking mechanism, real-time operating system (RTOS), and power transmission system. Results Picking efficiency increased significantly under higher picking unit rotational speeds and lower plant conveying speed. On the other side, chili pepper damage decreased significantly under lower picking unit rotational speeds and higher plant conveying speed. The plant conveying speed was 1.47 km/h and rotational speed of picking unit was 177.55 rpm considered for optimum performance output with a maximum picking efficiency of 78.17 % and minimum chili pepper damage of 2.62 %. Conclusions It has been observed that the comb-type picking mechanism was efficient in picking of chili pepper from chili plant with a maximum picking efficiency at optimal settings. Further retrofitting of such picking unit to a self-propelled agriculture machine to harvest chili pepper in actual field conditions and a replacement to conventional harvesting process.
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Rathore Divya,Divyanth L. G.,Reddy Kaamala Lalith Sai,Chawla Yogesh,Buragohain Mridula,Soni Peeyush,Machavaram Rajendra,Hussain Syed Zameer,Ray Hena,Ghosh Alokesh 한국농업기계학회 2023 바이오시스템공학 Vol.48 No.2
Purpose The process of robotic harvesting has revolutionized the agricultural industry, allowing for more effi cient and costeff ective fruit picking. Developing algorithms for accurate fruit detection is essential for vision-based robotic harvesting of apples. Although deep-learning techniques are popularly used for apple detection, the development of robust models that can accord information about the fruit’s occlusion condition is important to plan a suitable strategy for end-eff ector manipulation. Apples on the tree experience occlusions due to leaves, stems (branches), trellis wire, or other fruits during robotic harvesting. Methods A novel two-stage deep-learning-based approach is proposed and successfully demonstrated for detecting ontree apples and identifying their occlusion condition. In the fi rst stage, the system employs a cutting-edge YOLOv7 model, meticulously trained on a custom Kashmiri apple orchard image dataset. The second stage of the approach utilize the powerful Effi cientNet-B0 model; the system is able to classify the apples into four distinct categories based on their occlusion condition, namely, non-occluded, leaf-occluded, stem/wire-occluded, and apple-occluded apples. Results The YOLOv7 model achieved an average precision of 0.902 and an F1-score of 0.905 on a test set for detecting apples. The size of the trained weights and detection speed were observed to be 284 MB and 0.128 s per image. The classifi cation model produced an overall accuracy of 92.22% with F1-scores of 94.64%, 90.91%, 86.87%, and 90.25% for nonoccluded, leaf-occluded, stem/wire-occluded, and apple-occluded apple classes, respectively. Conclusion This study proposes a novel two-stage model for the simultaneous detection of on-tree apples and classify them based on occlusion conditions, which could improve the eff ectiveness of autonomous apple harvesting and avoid potential damage to the end-eff ector due to the objects causing the occlusion.
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