Automated disease classification in (Selected) agricultural crops using transfer learning

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

Task-based agricultural mobile robots in arable farming: A review

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 farmin

Disease classification in Solanum melongena using deep learning

Aim of study: The application of pre-trained deep learning models, AlexNet and VGG16, for classification of five diseases (Epilachna beetle infestation, little leaf, Cercospora leaf spot, two-spotted spider mite and Tobacco Mosaic Virus (TMV)) and a healthy plant in Solanum melongena (brinjal in Asia, eggplant in USA and aubergine in UK) with images acquired from smartphones.Area of study: Images were acquired from fields located at Alangudi (Pudukkottai district), Tirumalaisamudram and Pillayarpatti (Thanjavur district) – Tamil Nadu, India.Material and methods: Most of earlier studies have been carried out with images of isolated leaf samples, whereas in this work the whole or part of the plant images were utilized for the dataset creation. Augmentation techniques were applied to the manually segmented images for increasing the dataset size. The classification capability of deep learning models was analysed before and after augmentation. A fully connected layer was added to the architecture and evaluated for its performance.Main results: The modified architecture of VGG16 trained with the augmented dataset resulted in an average validation accuracy of 96.7%. Despite the best accuracy, all the models were tested with sample images from the field and the modified VGG16 resulted in an accuracy of 93.33%.Research highlights: The findings provide a guidance for possible factors to be considered in future research relevant to the dataset creation and methodology for efficient prediction using deep learning models

Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques—A Review

Article number 684328Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed.Ministerio de Economía y Competitividad ( España) CEI-15-AGR278, US-126367

Emitted Sound Amplitude Analysis Using Hilbert Huang Transformation for Cutting Tool Flank Wear Prediction

In this paper, the relationship between emitted sound amplitude and tool flank wear was investigated during turning operation by using Hilbert Huang Transform (HHT). For this purpose, a series of experiments using carbide insert cutting tools and stainless steel work pieces were conducted in a conventional turning machine. The emitted sounds from fresh, slightly worn and severely worn tools were recorded at the sampling rate of 44100/sec using a highly sensitive directional microphone. Each recorded multi-component sound signal was decomposed into several intrinsic mode functions (IMFs) using Empirical mode decomposition (EMD). The instantaneous frequencies with time and their amplitudes were obtained by applying Hilbert transform on each IMF. From the marginal spectrums produced using selected IMFs it was observed that there was an increase in sound amplitude with increasing tool wears. Hence it can be concluded that cutting tool flank wear prediction is possible using emitted sound by HHT

Drought, Low Nitrogen Stress, and Ultraviolet-B Radiation Effects on Growth, Development, and Physiology of Sweetpotato Cultivars during Early Season

Drought, ultraviolet-B (UV-B), and nitrogen stress are significant constraints for sweetpotato productivity. Their impact on plant growth and development can be acute, resulting in low productivity. Identifying phenotypes that govern stress tolerance in sweetpotatoes is highly desirable to develop elite cultivars with better yield. Ten sweetpotato cultivars were grown under nonstress (100% replacement of evapotranspiration (ET)), drought-stress (50% replacement of ET), UV-B (10 kJ), and low-nitrogen (20% LN) conditions. Various shoot and root morphological, physiological, and gas-exchange traits were measured at the early stage of the crop growth to assess its performance and association with the storage root number. All three stress factors caused significant changes in the physiological and root- and shoot-related traits. Drought stress reduced most shoot developmental traits (29%) to maintain root growth. UV-B stress increased the accumulation of plant pigments and decreased the photosynthetic rate. Low-nitrogen treatment decreased shoot growth (11%) and increased the root traits (18%). The highly stable and productive cultivars under all four treatments were identified using multitrait stability index analysis and weighted average of absolute scores (WAASB) analyses. Further, based on the total stress response indices, ‘Evangeline’, ‘O’Henry’, and ‘Beauregard B-14’ were identified as vigorous under drought; ‘Evangeline’, ‘Orleans’, and ‘Covington’ under UV-B; and ‘Bonita’, ‘Orleans’, and ‘Beauregard B-14’ cultivars showed greater tolerance to low nitrogen. The cultivars ‘Vardaman’ and ‘NC05-198’ recorded a low tolerance index across stress treatments. This information could help determine which plant phenotypes are desirable under stress treatment for better productivity. The cultivars identified as tolerant, sensitive, and well-adapted within and across stress treatments can be used as source materials for abiotic stress tolerance breeding programs

Management of Intra-Articular Distal Radius Fractures by Open Reduction Internal Fixation and Plate Osteosynthesis Vs Ligamentotaxis : A Comparative Study

AIM: To analyze and compare the functional and radiological outcome of different methods of surgical management of intra-articular distal radius fractures in 50 patients treated by either open reduction, internal fixation and plate osteosynthesis using distal radius volar locking plate or by closed reduction and ligamentotaxis using external fixator with the addition of k wires. MATERIALS AND METHODS: This prospective interventional study was conducted among 50 patients with distal radius intra-articular fracture, treated by either  open reduction internal fixation and plate osteosynthesis using distal radius volar locking plate or by closed reduction and ligamentotaxis using external fixator with the addition of k wires if needed in our institute between July 2020 and November 2022 over a period of 2 years and 5 months. RESULTS: In our study, there were 43 male and 7 female patients, most of whom were in the age group 30-50 years (48%). The mode of injury in most cases was RTA (Road traffic accident).Out of 50 patients, 25 were treated by Open reduction, internal fixation with Volar locking compression plate and 24 were treated by Closed reduction and external fixation augmented by K wire fixation (Ligamentotaxis). There was also found to be no significant difference in the radiological outcome in both groups of patients, i.e Radial length (p=0.253), Palmar tilt (p=0.08), Radial inclination (p=0.075) and articular step-off (p=0.207). In terms of functional outcome of both procedures, only significant difference was found to in palmar flexion of the wrist joint (p=0.003) with better range of palmar flexion seen in the Open reduction and internal fixation with volar locking compression plate group. There was no significant difference seen in dorsiflexion, ulnar and radial deviation, supination and pronation between both surgical techniques. We had a few complications such as malunion, pin loosening and wrist stiffness in our study, majority of which was seen in the Ligamentotaxis group, but it had no statistical significance. A mean DASH score of 11.788 was seen in the VLCP group and 15.192 was seen in the Ligamentotaxis group, the difference was not statistically significant

Hyperspectral Reflectance and Machine Learning Approaches for the Detection of Drought and Root–Knot Nematode Infestation in Cotton

Upland cotton encounters biotic and abiotic stresses during the growing season, which significantly affects the genetic potential of stress tolerance and productivity. The root-knot nematode (RKN) (Meloidogyne incognita) is a soilborne roundworm affecting cotton production. The occurrence of abiotic stress (drought stress, DS) can alter the plant–disease (RKN) interactions by enhancing host plant sensitivity. Experiments were conducted for two years under greenhouse conditions to investigate the effect of RKN and DS and their combination using nematode-resistant (Rk-Rn-1) and nematode susceptible (M8) cotton genotypes. These genotypes were subjected to four treatments: control (100% irrigation with no nematodes), RKN (100% irrigation with nematodes), DS (50% irrigation with no nematodes), and DS + RKN (50% irrigation with nematodes). We measured treatments-induced changes in cotton (i) leaf reflectance between 350 and 2500 nm; and (ii) physiology and biomass-related traits for diagnosing plant health under combined biotic and abiotic stresses. We used a maximum likelihood classification model of hyperspectral data with different dimensionality reduction techniques to learn RKN and DS stressors on two cotton genotypes. The results indicate (i) the RKN stress can be detected at an early stage of 10 days after infestation; (ii) RKN, DS, and DS + RKN can be detected with an accuracy of over 98% using bands from 350–1000 nm and 350–2500 nm. The genotypes ‘Rk-Rn-1’and ‘M8’ showed differential responses to DS, RKN, and DS + RKN. With a few exceptions, all three stressors reduced the pigments, physiology, and biomass traits and the magnitude of reduction was higher in ‘M8’ than ‘Rk-Rn-1’. Observed impact of stressors on plant growth followed DS + RKN > DS > RKN. Similarly, leaf reflectance properties exhibited a significant difference between individual stress treatments indicating that the hyperspectral sensor data can be used to discriminate RKN-infected plants from drought-stressed plants. Thus, our study reveals that hyperspectral and physiological changes in response to RKN and DS could help diagnose plant health before visual symptoms