The agricultural production is greatly affected by various plant diseases. Classifying the severity of crop diseases is the requirement for formulating disease prevention and control strategies. However, the differences between different severity of the same crop disease are very tiny. It increases the difficulty of correct crop disease recognition. For example, at the early stage of the disease, the lesions on the leaves are not obvious. And it is very difficult to extract the features of the lesions. However, these very small color and texture differences of the lesions are the key patterns to distinguish different kinds of diseases of the same species. In order to achieve better performance in the fine-grained classification of the crop diseases, a modified light-weight convolution neural network was proposed. Multi-scale convolution kernel and coordinate attention mechanism are introduced in SqueezeNext to extract the features of the lesions accurately. The performance of the proposed model was evaluated using the AI challenger 2018 plant disease recognition dataset, and the recognition accuracy can reach 91.94%, which is 3.02% point higher than the original SqueezeNext model. In order to verify the effectiveness of the proposed model, comparative experiments were carried out using ReseNet50, Xception and mobilenetv2. The experimental results showed that the accuracy of the proposed method was slightly better than Xception, while the model size is only 2.83 MB, which is only 3.45% of Xception. The proposed method balances the performance and efficiency very well. Thus, it is suitable for deployment on mobile terminals and other embedded resource-constrained devices, which help to promote the popularization of smart agriculture application.

This brief investigates the finite-time adaptive extended state observer (ESO) based dynamic sliding mode control (SMC) for the hybrid robot subject to uncertain problem, e.g., modeling error, joint friction, external disturbance and so on. A barrier function-based adaptive law is defined to adjust the switching gain of SMC system with unknown upper bound of the disturbance. Such an adaptive law-based control method ensures the finite-time convergence of the sliding variable to an adjustable vicinity of zero and realizes chattering reduction of SMC. Then, for estimating and compensating system uncertainties, the time-varying gain ESO (TESO) is designed without the condition of the disturbance change rate tending to zero. A system error-based adaptive law is designed to adjust the observer bandwidth gain, which can reduce the peaking value. Such a TESO reduces the burden of SMC and further weakens the chattering of SMC, so as to improve the control system robustness. The finite-time Lyapunov stability of the sliding variable and the boundness of the TESO error have been proved theoretically. The practical effectiveness is illustrated in simulations and experiments with the prototype of hybrid robot.

Precise and rapid grasping point detection based on machine vision is one of the challenging problems in automatic sorting of randomly placed fruit clusters by robot. Grasping stalk of fruit cluster can improve grasping success probability and reduce fruit damage. For the problem that the segmentation of stalk candidates for randomly placed fruit cluster based on existing morphology algorithm tends to be low precision, an improved image segmentation algorithm based on adaptive morphology is proposed. According to edge distances defined based on minimum distance between edge point and unconnected components in minimum domain, the adaptive convolution kernel is constructed. In addition, a run analysis method with different and unordered labels is designed to reduce calculation time of edge distances. For the problem that it is difficult to describe and classify unconstraint stalk by existing features, an improved region classification algorithm based on principal components of multiple features is proposed. The descriptors based on features of object region are designed and principal components of multiple features are extracted based on variance contribution to improve precision and speed of stalk extraction. The proposed grasping point detection method of randomly placed fruit cluster based on improved morphology image segmentation and region classification algorithms is verified by experiments with grape clusters based on parallel robot sorting system. The results show that, compared with existing methods, the average precisions of segmentation and extraction for stalk increase by 9.89% and 2.17% respectively, the average precision and time of grasping point detection reach 94.50% and 2.01s respectively.