A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

Abstract

for a project on "Application of IoT in Agriculture Sector" through the ICPS division under grant ID DST-319."ABSTRACT Agricultural productivity is the asset on which the world's economy thoroughly relies. This is one of the major causes that disease identification in fruits and plants occupies a salient role in farming space, as having disease disorders in them is obvious. There is a need to carry genuine supervision to avoid crucial consequences in vegetation; otherwise, corresponding vegetation standards, quantity, and productiveness gets affected. At present, a recognition system is required in the food handling industries to uplift the effectiveness of productivity to cope with demand in the community. The study has been carried out to perform a systematic literature review of research papers that deployed machine learning (ML) techniques in agriculture, applicable to the banana plant and fruit production. Thus; it could help upcoming researchers in their endeavors to identify the level and kind of research done so far. The authors investigated the problems related to banana crops such as disease classification, chilling injuries detection, ripeness, moisture content, etc. Moreover, the authors have also reviewed the deployed frameworks based on ML, sources of data collection, and the comprehensive results achieved for each study. Furthermore, ML architectures/techniques were evaluated using a range of performance measures. It has been observed that some studies used the PlantVillage dataset, a few have used Godliver and Scotnelson dataset, and the rest were based on either real-field image acquisition or on limited private datasets. Hence, more datasets are needed to be acquired to enhance the disease identification process and to handle the other kind of problems (e.g. chilling injuries detection, ripeness, etc.) present in the crops. Furthermore, the authors have also carried out a comparison of popular ML techniques like support vector machines, convolutional neural networks, regression, etc. to make differences in their performance. In this study, several research gaps are addressed, allowing for increased transparency in identifying different diseases even before symptoms arise and also for monitoring the abovementioned problems related to crops.

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