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The main agricultural commodity in Indonesia is shallots. This commodity is highly affected by weather conditions. Climate change causes these weather conditions to have an impact on disease. Controlling diseases on shallots quickly and precisely will help farmers in dealing with pests and diseases to avoid crop failure. Deep learning-based pest detection for the detection of disease symptoms from horticultural crops has developed. The detection of these symptoms provides predictions about the diseases present in shallots. This study proposes the detection of shallot disease using YOLOv3 which is an object detection algorithm based on deep learning. This research includes dataset selection, training process, and determination of detection model. symptom detection in this study, divided into 3 classes of disease symptoms. The yolov3 parameters observed were GIoU, objectless, loss classification, precision, recall, mAp, F1 score. Based on the results of the study, it was found that the precision level of yolov3 for the detection of onion disease symptoms was 59.4%. These results are good for the detection of shallot symptoms using deep learning. [ABSTRACT FROM AUTHOR]

Agriculture is one of the important sectors in Indonesia. There are several commodities consumed by Indonesian citizens, one of them is shallots as a cooking spice and herbal medicine. Several problems occasionally arise in the shallots cultivation process; one of them is increasing plant pest organism's attacks. However, the control process is still done conventionally. Therefore, it is necessary to build a system to diagnose diseases automatically. This paper proposes an information system for agriculture shallots based on machine learning using dempster Shafer. After collecting the data, dempster Shafer is applied to determine and define a reasonable level of confidence and a function to evaluate a possibility. Based on the experiment, the accuracyofmax belief for detection of shallots disease is 60%. By using several scenarios, the experimental result shows that dempster Shafer can detect shallot disease well. Furthermore, the algorithm can be implemented in web and mobile applications based to monitor the controlling process easier. [ABSTRACT FROM AUTHOR]