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Jujube is one of the popular fruits that possess high nutritional components and have economic value. Grading of jujube is a post-harvest process applied in the fruit industry for the tasks like fruit quality check, fruit species identification, price labelling, edibility duration estimation, safety, etc. This research investigates the proposed customised-CNN model and two classical models (i.e., VGG16 and AlexNet) for grading jujube fruits according to their stages of maturity. Primarily, jujube of four different maturity grades was identified on the field and collected from the field manually and their images were captured through a machine vision system. Further, image pre-processing and augmentation were performed to get the training/testing-ready dataset. Finally, a customised-CNN model was deployed and grading performance was examined over the original and augmented dataset using performance metrics of precision, sensitivity, and F1-measure. Furthermore, the model's classification accuracy was compared to that of classical models, where the proposed model surmounts both the classical models. Results reveal that the proposed model attained a high grading accuracy of 99.44% and 97.53% over the augmented and original datasets respectively. Also, the computation time and training parameters count were reduced to almost one-tenth and one-third of that of the VGG16 and AlexNet models. Results advocate that the classical model could be replaced with the proposed customized-CNN model and can be further investigated for other fruits for better classification accuracy, reduced parameters and reduced computational time.

Agricultural automation is an emerging subject today to accomplish the food demands of individuals across the globe. Machine learning is one such agricultural automation tool that has been adopted briskly in the recent decade due to its ability to process countless input data and handle non-linear tasks. Availability and continuous development of agricultural data led the machine learning pervasive in multiple aspects of agriculture. This paper systematically analyses and summarizes the 81 quality research efforts published in the past decade dedicated to the various contemporary machine learning applications in agriculture and food production systems. We examined and categorized each agricultural problem under study into four categories and each category into its subcategories. The finding demonstrates the contemporary applications of machine learning in broad agricultural subcategories and determines where it is heading shortly; based upon contributions of researchers, utilization of machine learning models/algorithms, and the availability of agricultural datasets.Through the analysis, it is discovered that the current innovation can help the improvement of agricultural automation to accomplish the advantages of minimal cost, high efficiency, and better precision. This paper can serve as an investigatory guide for researchers, academicians, engineers, and manufacturers to understand and apply modern and upgraded cognitive technologies to each subcategory of the agricultural sector.

Jujube is one of the popular fruits that possess high nutritional components and have economic value. Grading of jujube is a post-harvest process applied in the fruit industry for the tasks like fruit quality check, fruit species identification, price labelling, edibility duration estimation, safety, etc. This research investigates into the suggested 7-layer CNN model and two classical models (i.e., VGG16 and AlexNet) for grading jujube fruits according to their stages of maturity. Primarily, jujube of four different maturity grades was identified on the field and collected from the field manually and their images were captured through a machine vision system. Further, image pre-processing and augmentation were performed to get the training/testing-ready dataset. Finally, a 7-layer CNN model was deployed and grading performance was examined over the original and augmented dataset using performance metrics of precision, sensitivity, and F1-measure. Furthermore, the model’s classification accuracy was compared to that of classical models, where the proposed model surmounts both the classical models. Results reveal that the proposed model attained a high grading accuracy of 99.44% and 97.53% over the augmented and original dataset respectively. Also, the computation time and training parameters count were reduced to almost one-tenth and one-third of that of VGG16 and AlexNet models. Results advocate that the classical model could be replaced with the proposed models and can be further investigated for other fruits for better classification accuracy, reduced parameters and reduced computational time.