In agriculture (in the context of this paper, the terms "agriculture" and "farming" refer to only the farming of crops and exclude the farming of animals), smart farming and automated agricultural technology have emerged as promising methodologies for increasing the crop productivity without sacrificing produce quality. The emergence of various robotics technologies has facilitated the application of these techniques in agricultural processes. However, incorporating this technology in farms has proven to be challenging because of the large variations in shape, size, rate and type of growth, type of produce, and environmental requirements for different types of crops. Agricultural processes are chains of systematic, repetitive, and time-dependent tasks. However, some agricultural processes differ based on the type of farming, namely permanent crop farming and arable farming. Permanent crop farming includes permanent crops or woody plants such as orchards and vineyards whereas arable farming includes temporary crops such as wheat and rice. Major operations in open arable farming include tilling, soil analysis, seeding, transplanting, crop scouting, pest control, weed removal and harvesting where robots can assist in performing all of these tasks. Each specific operation requires axillary devices and sensors with specific functions. This article reviews the latest advances in the application of mobile robots in these agricultural operations for open arable farming and provide an overview of the systems and techniques that are used. This article also discusses various challenges for future improvements in using reliable mobile robots for arable farming.Additional key words: precision agriculture; task-based agricultural robots; soil analysis; seeding; weed detection; harvesting; crop scouting robot Correspondence should be addressed to P. Raja: raja_sastra@yahoo.com

No abstract

No abstract

The biotic stress of agricultural crops is a major concern across the globe. Especially, its major effects are felt in economically poor countries where advanced facilities for diagnosis of a disease is limited as well as lack of awareness among the farmers. A recent revolution in smartphone technology and deep learning techniques have created an opportunity for automated classification of disease. In this study images acquired through smartphone are transmitted to a personal computer via a wireless Local Area Network (LAN) for classification of ten different diseases using transfer learning in four major agricultural crops which are least explored. Six pre-trained Convolutional Neural Network (CNN) have been used namely AlexNet, Visual Geometry Group 16 (VGG16), VGG19, GoogLeNet, ResNet101 and DenseNet201 with its corresponding results explored. GoogLeNet resulted in the best validation accuracy of 97.3%. The misclassification was mainly due to Tobacco Mosaic Virus (TMV) and two-spotted spider mite. In test conditions, images were classified in real-time and prediction scores have been evaluated for each disease class. It depicted a reduction in accuracy in all models with VGG16 resulting in the best accuracy of 90%. Various factors contributing to the reduction in accuracy and future scope for improvement have been elucidated.

No abstract