Deep learning based computer vision approaches for smart agricultural applications

The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies.At the core of artificial intelligence,deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality.Computer vision techniques,in conjunction with high-quality image acquisition using remote cameras,enable non-contact and efficient technology-driven solutions in agriculture.This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting.Recent works in the area of computer vision were analyzed in this paper and categorized into(a)seed quality analysis,(b)soil analysis,(c)irrigation water management,(d)plant health analysis,(e)weed management(f)livestock management and(g)yield estimation.The paper also discusses recent trends in computer vision such as generative adversarial networks(GAN),vision transformers(ViT)and other popular deep learning architectures.Additionally,this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time.The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy.However,the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.

Automation and digitization of agriculture using artificial intelligence and internet of things

The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity.In most of the countries where the expansion of cropland is merely impossible,agriculture automation has become the only option and is the need of the hour.Internet of things and Artificial intelligence have already started capitalizing across all the industries including agriculture.Advancement in these digital technologies has made revolutionary changes in agriculture by providing smart systems that can monitor,control,and visualize various farmoperations in real-time andwith comparable intelligence of human experts.The potential applications of IoT and AI in the development of smart farmmachinery,irrigation systems,weed and pest control,fertilizer application,greenhouse cultivation,storage structures,drones for plant protection,crop health monitoring,etc.are discussed in the paper.The main objective of the paper is to provide an overview of recent research in the area of digital technology-driven agriculture and identification of the most prominent applications in the field of agriculture engineering using artificial intelligence and internet of things.The research work done in the areas during the last 10 years has been reviewed from the scientific databases including PubMed,Web of Science,and Scopus.It has been observed that the digitization of agriculture using AI and IoT hasmatured fromtheir nascent conceptual stage and reached the execution phase.The technical details of artificial intelligence,IoT,and challenges related to the adoption of these digital technologies are also discussed.This will help in understanding how digital technologies can be integrated into agriculture practices and pave the way for the implementation of AI and IoT-based solutions in the farms.

Deep convolutional neural network models for weed detection in polyhouse grown bell peppers

Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.

Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar,India

Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning.Its quantification is helpful in irrigation scheduling,water balance studies,water allocation,etc.Modelling of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 models.The model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the models.All the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing phase.It was found that ANN models were superior to GEP models for the estimation purpose.It was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP models.The ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP models.The comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.