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Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [ABSTRACT FROM AUTHOR]

Drones have been of vital importance in the fields of surveillance, mapping, and infrastructure inspection. Drones have played a vital role in acquiring high-resolution images and with the present need for precision farming, drones have helped in crop classification and monitoring various crop patterns. With the recent advancement in computational power and development of robust algorithms to carry out deep feature learning and neural network, based learning such techniques have regained prominence in contemporary research areas such as classification of common 2-D and 3-D images, object detection, etc. In our research, we propose a deep convolutional neural network architecture (CNN) for the classification of aerial images captured by drones and high-resolution Terrestrial Hyperspectral (THS or HSI) which includes 6-layers and with weights optimized along with the input layer, the convolutional layer, the max-pooling layer, the fully connected layer, softmax probability classifier, and the output layer. We have acquired THS (using Cubert-GmbH data) and drone agricultural data of seasonal crops sowed during the months of March-June for the year 2017. Crop patterns include Cabbage, Eggplant, and Tomato with varying nitrogen concentrations in the region of Bangalore, Southern India. To study the influence and impact of CNN, the ResNets model has been applied. ResNets model and architecture are combined with a deep learning network followed by a recurrent neural learning network model (RCNN). The HSI input layer with corresponding ground truth data for the region is fed into the ResNets model with a spectral and spatial residual network for the 7*7*139 input Hyperspectral Imagery (HSI) volume. The network includes two spectral and two spatial residual blocks. An average pooling layer and a fully connected layer transform into a 5*5*24 spectral-spatial feature volume further to a single output feature vector. At present we use an RMSProp optimizer for error loss minimization which when applied to the drone data was able to achieve an overall accuracy of 97.16%. Similarly, for cabbage, eggplant and tomato acquired through the same method we achieved overall accuracy at 87.619%, 89.25%, and 80.566% respectively in comparison to ground truth labels. Drones and ground-based datasets equipped with good computational techniques have become promising tools for improving the quality and efficiency of precision agriculture today. [ABSTRACT FROM AUTHOR]